Join us to Create
and Build the Digital Future.

purpleHeads are primarily driven by passion. Passion to learn, write code, build things, keep it
simple and live a happy life. We are fortunate to have some of the talented individuals with rich
experience from Fortune 100 companies, working with young, fearless minds.

We are purpleSlate - driven by passion, proven by results

Often, the true value of an idea is not the idea itself. Rather, it is the intent or the WHY that led to the creation of it.

At purpleSlate, we are eternal optimists and we strongly believe the Future is as much about the exciting opportunities that lies ahead of us in leveraging technology to augment human capability, that helps solve some of the complex problems faced by the next billion. As machines become smarter, our abilities need to scale up the knowledge chain.

It is no longer on WHAT you build but, as much about HOW you build software.

How can I join Purpleslate?

We hire people for not who they were; but more for who they could become

About Us

Send an email to hello@purpleSlate.com or pick a time to reach our team. We would be more than happy to assist.

Just like any startup, we had our humble beginnings, starting with a team of two members (the co-founders) and today we are a passionate team of 100+.

We are a technology startup based out of Chennai, India. We are located at a quiet campus in one of the prime technology centers in Chennai. You can find us here. https://goo.gl/maps/cGJ53NHUfij8yLXR9 

I know, you would ask this. So, it goes like this. As per the color theory, the color Purple indicates all things good and positive in life. And, as a company we at purpleSlate sincerely believe in continuous learning and sticking to the basics and the same logic applies to machines, as AI goes mainstream. As many of the Indians might know, Slate, is one of the very first learning instruments we are exposed to in life. Hence the name purpleSlate. I know, it sounds a bit too much. But, we seriously meant it.

Good question. So, it goes like this. As per the color theory, the color Purple indicates all things good and positive in life. And, as a company we at purpleSlate sincerely believe in continuous learning and sticking to the basics. As many of the Indians might know, Slate, is one of the very first learning instruments we are exposed to in life. Hence the name purpleSlate. I know, it sounds a bit too much. But, we seriously meant it.

What else can I tell you? Hmm. Let me think. The company was founded on 2nd Jan 2017 and has had an incredible growth journey since them. Almost every year we have added new team members, moved into a new, larger office space, which means we have been very much profitable as well. We really believe you can do great things, quietly.

Good question. We are a bunch of geeks and we solve challenging industry problems leveraging modern day digital technologies. We specialize on Language Interfaces and Conversational AI. Not a far fetched claim; we feel (almost) any problem can be codified and we are up for it.

Careers

You can reach us at people@purpleslate.com If you share your work, we guarantee that we respond within a day or two if we find your profile to be suitable for us. Or, if you know of anyone who be a great fit for any of these roles, please do let us know or spread the message.

Yes, we do hire from campuses and we believe firmly in grooming our own talent from schools.

We hire people for not who they were; but more for who they could become. Hence, for that same reason we invest a good amount of our our time in curating our own talent from schools, that too not from the usual big name institutions (not that we don’t like them). But, from any place that is as anywhere it can get. School pedigree, doesn’t matter much to us.

Besides being good engineers, many of us are trying to be good teachers and mentors. We find nurturing untapped potential and setting them up on a good career path is far more fulfilling. It may take lots of patience to grow and nurture our own talent. But, we feel it is worth the effort it takes and we are proud to see the way some of our campus hires have grown with us to deliver some great work.

Our same hiring philosophy applies even for Campus Hires. Before you shoot out an email, do remember to share your Github profile or other work that you are proud of. Let your work speak more than your resume and certificates.

First of all, a Hearty Welcome if you have come this far and reading this.

We believe great talent and people can come from unlikely places. Most of us working in purpleSlate are testimony to that fact. Hence, we believe focusing on the person, their work, their interests and how they can help us is the most right way to bring the best talent.

We also believe, these days, you can’t land a job just by submitting your resume. We really don’t care where you studied or where you worked. We love to see your work – be it coding skills or anything else that you are endowed with and which you feel would add value to purpleSlate. So, share with us your Github repo or other stuff that you built and that you are proud of.

If you feel, you don’t have anything that can be shared, (for multiple reasons), we respect that. Do let us know and we would be happy to have you work on one of our own projects for a very short time (2-4 weeks). In fact some of our colleagues interned with us for nearly 4-6 months before joining us. We would be more than happy to even pay you for your time.

If there is any interesting and cutting-edge technology available in the horizon, we will most likely be tinkering with it, as we constantly experiment and refine our approach to solve some interesting problems. Scroll down a bit to see the technologies that we are currently playing with.

In addition, we are also looking for folks with expertise in Test Automation, using tools like Selenium and creative people who are good at stuff like Content Writing and creating digital content, including Videos, and illustrations.

We believe any time is a good time for great talent to show up on our doors and that way, we are ready, always.

Is your answer predominantly YES to all of these? Then read ahead.

  • I like solving problems.
  • I am good at creating things.
  • I can write beautiful code.
  • I am passionate about technology.
  • I like working in small teams.
  • I am a quick learner.
  • I want to leave an impact.

Very simple. Is your answer predominantly YES to all of these? Then, say Hello at people@purpleslate.com

I like solving problems.
I am good at creating things.
I can write beautiful code.
I am passionate about technology.
I like working in small teams.
I am a quick learner.
I want to leave an impact.

We also believe, these days, you can’t land a job just by submitting your resume. We really don’t care where you studied or where you worked. We love to see your work – be it coding skills or anything else that you are endowed with and which you feel would add value to purpleSlate. So, share with us your Github repo or other stuff that you built and that you are proud of.

Yes, we do hire from campuses and we believe firmly in grooming our own talent from schools.

We hire people for not who they were; but more for who they could become. Hence, for that same reason we invest a good amount of our our time in curating our own talent from schools, that too not from the usual big name institutions (not that we don’t like them). But, from any place that is as anywhere it can get. School pedigree, doesn’t matter much to us.

Of course, yes. We always believe anytime is a great time to welcome great talent. Say Hello at people@purpleslate.com Or, to make it very simple, share with us your Github repo or other stuff that you built and that you are proud of.

If there is any interesting and cutting-edge technology available in the horizon, we will most likely be tinkering with it, as we constantly experiment and refine our approach to solve some interesting problems. Scroll down a bit to see the technologies that we are currently playing with.

In addition, we are also looking for folks with expertise in Test Automation, using tools like Selenium and creative people who are good at stuff like Content Writing and creating digital content, including Videos, and illustrations.

Conversational AI

While the vendor Speech and Language Understanding Services are good with regular English vocabulary, the biggest challenge for enterprises to achieve reliable and predictable accuracy levels is in handling specialized vocabulary in their own domain, its usage in day-to-day operations by their staff. As an example, think of the medical vocabulary, the drug names, medical conditions or the various part-names in an automobile company. How would your chatbot respond to dialogs with medical terms like Cutaneous, thorax, glossopharyngeal, ventroflexion, Annealing, Austentization? That’s what we call as domain specific vocabulary and it is very important for your Conversational AI application to have a deep maturity in language understanding on this vocabulary.

Garbage In – Garbage Out is very much a possibility in the case of Natural Language Understanding (NLU), because if we don’t process the text correctly, we will end up getting unwanted and irrelevant results from our applications and systems. It is not an easy task for machines to understand the language humans speak and the way we communicate. It is the nature of the human language that makes it difficult.

Smart Transcription focuses more on the Understanding part and achieving higher levels of accuracy in that.

Our customers come from diverse industries, but we have deep expertise building applications in Banking, Insurance, Healthcare, Petcare, Engineering, Education.

Awesome. We would be happy to show you our wares. Please pick a time here.

From the earliest days of the invention of computers, one of the fundamental question that has driven their evolution is how would the humans interact with the devices? To perform a computation, to get an answer or to get things done? So much so that, the digital screens across all form factors have taken over our lives. There is almost a screen for anything we do in our life. On the other side, Conversations is in the middle of everything we do and human language is one of the most primal skill. With the ever increasing ability of computers and devices to understand the language we speak, there is no better way and no better time than to reimagine the way we built interfaces – in the language we speak.

Ha, ha. Good question. We call it as ‘Get over the BOT hangover’. Not that we have a prejudice against any of the chatbots of today. As much as the chatbots have become so ubiquitous today, the user experience of many of these chatbot implementations are far from desirable. It is our earnest goal to help improve the user experience and make the chatbot more useful. You can more about it here.

It depends. While our low-code platform accelerators help you with the desired time to market and get your conversational AI solution up and running in no time, there are other factors that affect the implementation costs. We would be more than happy to demystify the costs and please reach us here. One of the purpleHead would be happy to assist soon.

Good question. You can find the answer here.

We do have quite a bit of interesting solutions delivered for many of our customers across the globe. Please schedule a demo here and we would be happy to share some of our success stories with our customers.

Data Glossary

Exploratory Data Analysis called by its abbreviation EDA is one of the initial steps a data scientist or data engineer does when they are presented with a new data set. This is the initial investigation aimed at understanding the characteristics of the data, the relationships between variables, test hypotheses, and test presumptions about the data with statistical graphs and other data visualization tools.

Data Mart is a data storage unit aimed at retaining data for a specific business line or department. Summarized data of a specific business function is kept within the data mart for ease of data access. For example, for the accounting department to close that year’s books, they can easily access a data mart to get specialized access to specific data sets.

A dashboard is a tool used by organizations to group various data relations and visually represent them in different graphical formats based on business requirements to track the performance of key variables. There are some self-serving tools like Tableau and PowerBI that enable data analysts to create dashboards based on required formats with various visualization options.

Data Lake is a centralized repository in which enterprise-wide data can be structured, semi-structured, or unstructured are saved. Data Lake ensures access restrictions pending authorization along with improving the ease of data access. The data can be stored in its native format without the need for structuring it and various types of analytics can be run on it inclusive of big data processing, data visualization, dashboard creations, etc. Data lakes are highly scalable and complex data operations can be performed inside them.

Correlation analysis is an advanced statistical technique used to measure the relationship between two variables. A high point indicates a strong correlation between the two variables. This is majorly employed during the quantitative analysis of the data points collected through methods like polls, surveys, etc. A simple example of correlation analysis would be to check the sales data of Thor merchandise concerning the sale of Thor: Love and Thunder tickets.

A recent technological advancement in the field of data analytics, behavior analytics helps in revealing consumer behavior insights across platforms like eCommerce, online games, web applications, etc. This will help businesses tailor their services or offerings to resonate with the end-user.

Being the final stage of the data analytics maturity curve, prescriptive analytics feeds on the results of descriptive, diagnostic, and predictive analytics results to suggest a probable cause of action and help businesses make informed decisions. Jack-riding on the previous example, if the individual was a serial defaulter, then the system can suggest the mortgage officer not to sanction loans for the individual as he has a history of defaulting and his credit scores are a mess.

As the name suggests, predictive analytics is the science of predicting future scenarios in business based on historical data. It relies on advanced statistical analysis, data mining, and machine learning for the system to come out with a comprehensive prediction.  It helps business leaders in data-driven decision-making and proactively mitigating risks. An everyday example would be analyzing a potential candidate’s past payment behavior and predicting on-time payment probability for a bank to extend credit lines.

The next step in the analytics journey, diagnostic analytics is aimed at understanding the “why” behind an occurrence. Studying the cause will help organizations mitigate or plan better for their future. Diagnostic analytics uses data drilling, data mining, and correlation analysis to uncover underlying causes. A simple example would be when product marketing teams are planning for a product launch campaign, diagnostic analytics reports of previous campaigns will help them plan better.

Often called the most basic form of data analytics, descriptive analytics deals with breaking down big numbers into consumable smaller formats. It helps in understanding basic trends but doesn’t help with deeper analysis. It stops at answering the “what”. Small everyday operations rely on descriptive analytics for their day-to-day planning.

As opposed to previous years, data has taken over the way businesses operate. It’s at the forefront of the world powered by information. Organizations use this data, process it, analyze it, and derive meaningful insights from it to aid in decision making. This process of leveraging technology to make data-driven decisions that will positively impact the business and revenue is termed Business Intelligence.

Data profiling deals with understanding the statistics and traits of a dataset, such as its accuracy, completeness, and validity. Data profiling helps in data validation and data cleansing efforts, as it helps detect quality issues like duplication, data errors, missing data points, and inconsistencies.

Data mining is a tool in the broader spectrum of data analytics that helps by sifting through large data sets to identify patterns and relationships. For example, data mining might reveal the most common factors associated with a rise in insurance claims. Data mining can be conducted manually or automatically with machine learning technology.

The process of crunching data to understand patterns and trends from which meaningful insights for business decisions can be derived. The results are generally represented in a visual format like graphs or charts, which are later incorporated into various reports or dashboards. There are 4 stages of evolution in the data analytics maturity curve – Descriptive, Diagnostic, Predictive, and Prescriptive.

Master data management (MDM) is a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency, and accountability of the enterprise’s official shared master data assets.  It includes policies and procedures for defining, managing, and controlling (or governing) the handling of master data. Centralized master data management eliminates conflict and confusion that stems from scattered databases with duplicate information and uncoordinated data that might be out-of-date, corrupted, or displaced in time – updated in one place but not in another. Master data is the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies, and a chart of accounts.

Data architecture is a framework of models, policies, and standards used by an organization to manage the data flow. Data has to be regularly cleaned to improve ease of access which will help other team members. Successful data architecture standardizes the processes to capture, store, transform and deliver usable data to people who need it. It identifies the business users who will consume the data and their varying requirements.

Data stewardship is the process of implementing and controlling data governance policies and procedures to ensure data accuracy, reliability, integrity, and security. The data leaders overseeing data stewardship have control over the procedures and tools used to handle, store, and protect data.

Data Governance is a set of principles that ensures consistency and reliability of the data. Data Governance takes stock of legal and regulatory requirements, along with introducing and adhering to industry best practices. Data Governance is also highly subjective as it encompasses the organization’s process standards as part of its structure. This includes the process of loading and storing data, access restrictions, and more.

Data integrity ensures that data stays unchanged over the long term. After the data is entered into the database after prior steps like data cleansing, wrangling, and validation, users can rest assured that data that has gone in will not change for whatsoever reason. This statement is what data integrity provides. Even though data integrity deals with reliability and dependability, sometimes it is also considered a synonym for data quality.

Data cleansing also called data scrubbing is the process of fixing incorrect data, clearing duplicate records, correcting incomplete data, etc. to ensure that the organization will have access to clean, accurate and usable data. This is a major step in data analysis as erroneous data can have catastrophic effects in the longer run. A simple example would be when Salespeople enter a proper noun in two different ways, like the name of a person is spelled differently using ‘y’ and ‘i’ by two different sales representatives. This leads to duplication of records and shows the sales figures as jacked up and falsifies revenue in the system.

Data validation is the process of ensuring that the data is cleansed, possesses a quality, and is accurate and valid to determine its usefulness before consuming it for analytics and reporting purposes. Data authentication, data cleansing, and making sure data is free of errors and duplicates are key steps while implementing data validation. Businesses need trustworthy, accurate, and quality data for decision making, and data validation ensures precisely that.

Data quality determines the reliability and usefulness of the data. Data is of good quality when it satisfies the five requirements – Accuracy, Completeness, Consistency, Reliability, and Recency. Ensuring data quality is determined by implementing an end-to-end data strategy supported by industry standards, best practices, tools, and systems with a fool-proof data management policy.

Data privacy deals with the activity of restricting and controlling relevant data access within and outside the organization. This policy determines the kind of data that can be accessed and stored within the organization’s database, the level of consent required, and other regulatory requirements set by various governing bodies.

Data security is the practice of protecting data from unauthorized access or exposure, disaster, or system failure, data corruption, data theft, and more. It also ensures that data are readily accessible to approved users and applications. It spans from physical data security methods to policies set around data security. Data encryption, key management, redundancy and backup practices, and access controls are some methods used. Amidst the rising data threats and privacy concerns, data security plays a central role. Data backup is a major requirement of Business Continuity Plans (BCP).

Data wrangling also called Data Munging is the process of transforming and mapping raw data into a format that databases and applications can access and read. The process may include structuring, cleaning, enriching, and validating data as necessary to make raw data useful. This process ensures that data is appropriate for downstream activities like analytics and reporting.

Data silos denote a situation where data access from other departments or functions is restricted. This can prove quite disastrous, as businesses would be impacted in terms of cost, inability to predict market changes, and losing agility to respond to market fluctuations. Data silos also lead to duplication of data, which leads to gaps in coordination between teams. 

A data pipeline defines the flow of data from a source to its intended target via different elements connected in series. The output from one element is considered to be the input of the next component. Data pipeline helps us connect various data storage systems and data flow can be automated to happen at specific intervals.

Data fabric is a combination of architecture and technology to break data silos and improve ease of data access for self-service data consumption. This concept is agnostic to location, sources, and data types, and enhances the end-to-end data management capabilities. It also automates data discovery, governance, and consumption enabling companies to quickly access and share data regardless of where it is or how it was generated.

Data from disparate sources and formats are unified in a virtual layer. This process is called data virtualization. It centralizes data security and governance and delivers data in real-time to the users. This saves time in duplicating data and helps users discover, access, act on and manipulate data in real-time regardless of its physical location, format, or protocol.

Data integration is the process of bringing data from multiple sources to a single source of truth. This is aimed at breaking data silos across the enterprise and beyond – including partners as well as third-party data sources and use cases. Techniques include bulk/batch data movement, extract, transform, load (ETL), change data capture, data replication, data virtualization, streaming data integration, data orchestration, and more.

Extremely large datasets consisting of structured, unstructured, and semi-structured data that traditional data processing software cannot handle are called big data. Big Data is defined by its 5 V’s – Velocity, Veracity, Volume, Variety, and Value. Velocity at which data is generated, Veracity to which data conforms, Volume of data handled, Variety of data types stored, and finally the Value the data provides in a business context. There are dedicated systems to mine Big Data for deep insights that aid in data-driven decision-making.

A data warehouse is a comprehensive single source of storage where data flows from different sources – both internal and external. Data engineers and other stakeholders access data for business intelligence (BI), reporting, and analytics through a data warehouse. A modern data warehouse plays a central role in data-driven decision-making and can manage all data types, structured and unstructured. They are cloud-ready to enable all-time access.

The process of visually representing data flows within the system as a whole or as parts to understand the connections between data points and structures. Data Modeling helps us understand the relationship between various data types, and how they can be organized or grouped based on attributes. From this flow diagram, software engineers can define the characteristics of the data formats, structures, and database handling functions to efficiently support the data flow requirements.

Data mapping helps in matching fields from one database or data structure into another. Mostly considered as the primary step, this has to be executed to enable smooth data migration, data integration, or other data management actions. This is essentially helpful when data is populated from multiple sources. Most data analytics tools are designed to be a single source of truth (one source for all data queries), and this ensures that there is consistency in the data being processed by removing duplicates, conflicting data, etc.

A simple definition of semi-structured data is data that can’t be organized in relational databases or doesn’t have a strict structural framework, yet does have some structural properties or a loose organizational framework. Semi-structured data includes text that is organized by subject or topic or fits into a hierarchical programming language, yet the text within is open-ended, having no structure itself. A good example of semi-structured data is e-mail – which includes some structured data, like the sender and recipient addresses, but also unstructured data, like the message itself.

Any data that does not conform to predefined standards, and lacks structure or architecture is called unstructured data. It is not organized into rows and columns – making it more difficult to store, analyze, and search. They are not stored in a relational database and access is tough. Examples include raw Internet of Things (IoT) data, video and audio files, social media comments, and call center transcripts. Unstructured data is usually stored in data lakes, NoSQL databases, or modern data warehouses.

When data is set in a standardized format, with a well-defined structure complying with a data model and guarantees ease of access, it is termed structured data. This can be a simple excel sheet to data accessed from a relational database. Financial transaction information, geographic climatic information, and demographic targeting for marketing, all can be classified as structured data. 

Data that is not stored in a tabular format depend on the NoSQL database. Unlike relational databases, a variety of forms can be supported in a NoSQL database – document, key-value, wide-column, and graph to name a few. They have excellent scalable capabilities and hence can handle high volumes of data. A NoSQL database is high in demand specifically for web 2.0 companies.

A relational database collects information that is organized in predefined relationships. The data is stored in a tabular format which defines the relationships between various data points. Relational databases use structured query language (SQL) to let administrators communicate with the database, join tables, insert and delete data, and more.

A database management system (DBMS) aids in the creation and management of databases. It handles the storage and data management part of the system. Many data manipulation operations are performed by users in a DBMS. All the applications dependent on the DBMS must be integrated to ensure the smooth functioning of both the application and the system. The DBMS is essentially a toolkit for database management.

Data management is a collection of processes required to collect, store, control, protect, operate on, and deliver data. The system is built up of a network of data storage units (databases, data warehouses, data marts, etc.), data collection tools, data retrieval mechanisms, and tools/processes that determine data governance. The entire system is then integrated with data analytics tools to derive meaningful insights. A data strategy also forms a core part of the data management function where it strives to establish accountability for data that originates or is endemic to particular areas of responsibility.

A database is a facility used to organize, store, manage, safeguard, and control access to data. Database designs depend on different schemes (schema), defined by the relational model designed for ease of access by programs and data queries. Some database examples include relational database management systems (RDBMS), in-memory databases, object-oriented databases (OODBMS), NoSQL databases, and NewSQL databases. Each of these has its own set of pros and cons.

Data is a collection of qualitative or quantitative values for drawing references that aids in decision-making. Examples of organizational data are sales figures, CAPEX spends, OPEX spends, etc.

We are always on the lookout for GREAT talent.

Anytime is a great time to welcome great talent

Open Job Positions

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Chennai
Data Scientist

Senior Full-Stack Engineer

  • Full-Time
  • Chennai
  • January 28, 2022
Mortarboard

Fresh from Campus

  • Full-Time
  • Chennai
  • January 29, 2022
Data Scientist

Data Engineer

  • Full-Time
  • Chennai
  • April 27, 2022

Join the fun.
Building something GREAT is the goal.

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