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• Key responsibility is to design & develop a data pipeline for real-time data integration, processing, executing of the model (if required), and exposing output via MQ / API / No-SQL DB for consumption
• Provide technical expertise to design efficient data ingestion solutions to store & process unstructured data, such as Documents, audio, images, weblogs, etc
• Developing API services to provide data as a service
• Prototyping Solutions for complex data processing problems using AWS cloud-native solutions
• Implementing automated Audit & Quality assurance Checks in Data Pipeline
• Document & maintain data lineage from various sources to enable data governance
• Coordination with BIU, IT, and other stakeholders to provide best-in-class data pipeline solutions, exposing data via APIs, loading in down streams, No-SQL Databases, etc

Skills

• Programming experience using Python & SQL
• Extensive working experience in Data Engineering projects, using AWS Kinesys,  AWS S3, DynamoDB, EMR, Lambda, Athena, etc for event processing
• Experience & expertise in implementing complex data pipeline
• Strong Familiarity with AWS Toolset for Storage & Processing. Able to recommend the right tools/solutions available to address specific data processing problems
• Hands-on experience in Unstructured (Audio, Image, Documents, Weblogs, etc) Data processing.
• Good analytical skills with the ability to synthesize data to design and deliver meaningful information
• Know-how on any No-SQL DB (DynamoDB, MongoDB, CosmosDB, etc) will be an advantage.
• Ability to understand business functionality, processes, and flows
• Good combination of technical and interpersonal skills with strong written and verbal communication; detail-oriented with the ability to work independently

Functional knowledge

• Real-time Event Processing
• Data Governance & Quality assurance
• Containerized deployment
• Linux
• Unstructured Data Processing
• AWS Toolsets for Storage & Processing
• Data Security

ABOUT EPISOURCE:

Episource has devoted more than a decade in building solutions for risk adjustment to measure healthcare outcomes. As one of the leading companies in healthcare, we have helped numerous clients optimize their medical records, data, analytics to enable better documentation of care for patients with chronic diseases.

The backbone of our consistent success has been our obsession with data and technology. At Episource, all of our strategic initiatives start with the question - how can data be “deployed”? Our analytics platforms and datalakes ingest huge quantities of data daily, to help our clients deliver services. We have also built our own machine learning and NLP platform to infuse added productivity and efficiency into our workflow. Combined, these build a foundation of tools and practices used by quantitative staff across the company.

What’s our poison you ask? We work with most of the popular frameworks and technologies like Spark, Airflow, Ansible, Terraform, Docker, ELK. For machine learning and NLP, we are big fans of keras, spacy, scikit-learn, pandas and numpy. AWS and serverless platforms help us stitch these together to stay ahead of the curve.

ABOUT THE ROLE:

We’re looking to hire someone to help scale Machine Learning and NLP efforts at Episource. You’ll work with the team that develops the models powering Episource’s product focused on NLP driven medical coding. Some of the problems include improving our ICD code recommendations, clinical named entity recognition, improving patient health, clinical suspecting and information extraction from clinical notes.

This is a role for highly technical data engineers who combine outstanding oral and written communication skills, and the ability to code up prototypes and productionalize using a large range of tools, algorithms, and languages. Most importantly they need to have the ability to autonomously plan and organize their work assignments based on high-level team goals.

You will be responsible for setting an agenda to develop and ship data-driven architectures that positively impact the business, working with partners across the company including operations and engineering. You will use research results to shape strategy for the company and help build a foundation of tools and practices used by quantitative staff across the company.

During the course of a typical day with our team, expect to work on one or more projects around the following;

1. Create and maintain optimal data pipeline architectures for ML

2. Develop a strong API ecosystem for ML pipelines

3. Building CI/CD pipelines for ML deployments using Github Actions, Travis, Terraform and Ansible

4. Responsible to design and develop distributed, high volume, high-velocity multi-threaded event processing systems

5. Knowledge of software engineering best practices across the development lifecycle, coding standards, code reviews, source management, build processes, testing, and operations  

6. Deploying data pipelines in production using Infrastructure-as-a-Code platforms

7. Designing scalable implementations of the models developed by our Data Science teams  

8. Big data and distributed ML with PySpark on AWS EMR, and more!

BASIC REQUIREMENTS 

1.  Bachelor’s degree or greater in Computer Science, IT or related fields
   
   

2.  Minimum of 5 years of experience in cloud, DevOps, MLOps & data projects
   
   

3. Strong experience with bash scripting, unix environments and building scalable/distributed systems
   
   

4. Experience with automation/configuration management using Ansible, Terraform, or equivalent
   
   

5. Very strong experience with AWS and Python
   
   

6. Experience building CI/CD systems
   
   

7. Experience with containerization technologies like Docker, Kubernetes, ECS, EKS or equivalent

8. Ability to build and manage application and performance monitoring processes

We are looking for a Data Analyst that oversees organisational data analytics. This will require you to design and help implement the data analytics platform that will keep the organisation running. The team will be the go-to for all data needs for the app and we are looking for a self-starter who is hands on and yet able to abstract problems and anticipate data requirements.
This person should be very strong technical data analyst who can design and implement data systems on his own. Along with him, he also needs to be proficient in business reporting and should have keen interest in provided data needed for business.

Tools familiarity:  SQL, Python, Mix panel, Metabase, Google Analytics,  Clever Tap, App Analytics

Responsibilities

• Processes and frameworks for metrics, analytics, experimentation and user insights, lead the data analytics team
• Metrics alignment across teams to make them actionable and promote accountability
• Data based frameworks for assessing and strengthening Product Market Fit
• Identify viable growth strategies through data and experimentation
• Experimentation for product optimisation and understanding user behaviour
• Structured approach towards deriving user insights, answer questions using data
• This person needs to closely work with Technical and Business teams to get this implemented.

Skills

• 4 to 6 years at a relevant role in data analytics in a Product Oriented company
• Highly organised, technically sound & good at communication
• Ability to handle & build for cross functional data requirements / interactions with teams
• Great with Python, SQL
• Can build, mentor a team
• Knowledge of key business metrics like cohort, engagement cohort, LTV, ROAS, ROE

Eligibility

BTech or MTech in Computer Science/Engineering from a Tier1, Tier2 colleges

Good knowledge on Data Analytics, Data Visualization tools. A formal certification would be added advantage.

We are more interested in what you CAN DO than your location, education, or experience levels.

Send us your code samples / GitHub profile / published articles if applicable.

Responsibilities :

• Involve in planning, design, development and maintenance of large-scale data repositories, pipelines, analytical solutions and knowledge management strategy
• Build and maintain optimal data pipeline architecture to ensure scalability, connect operational systems data for analytics and business intelligence (BI) systems
• Build data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader
• Reporting and obtaining insights from large data chunks on import/export and communicating relevant pointers for helping in decision-making
• Preparation, analysis, and presentation of reports to the management for further developmental activities
• Anticipate, identify and solve issues concerning data management to improve data quality

Requirements :

• Ability to build and maintain ETL pipelines 
• Technical Business Analysis experience and hands-on experience developing functional spec
• Good understanding of Data Engineering principles including data modeling methodologies
• Sound understanding of PostgreSQL
• Strong analytical and interpersonal skills as well as reporting capabilities

• Collaborate with the business teams to understand the data environment in the organization; develop and lead the Data Scientists team to test and scale new algorithms through pilots and subsequent scaling up of the solutions
• Influence, build and maintain the large-scale data infrastructure required for the AI projects, and integrate with external IT infrastructure/service
• Act as the single point source for all data related queries; strong understanding of internal and external data sources; provide inputs in deciding data-schemas
• Design, develop and maintain the framework for the analytics solutions pipeline
• Provide inputs to the organization’s initiatives on data quality and help implement frameworks and tools for the various related initiatives
• Work in cross-functional teams of software/machine learning engineers, data scientists, product managers, and others to build the AI ecosystem
• Collaborate with the external organizations including vendors, where required, in respect of all data-related queries as well as implementation initiatives

Role : 

• Understand and translate statistics and analytics to address business problems
• Responsible for helping in data preparation and data pull, which is the first step in machine learning
• Should be able to do cut and slice data to extract interesting insights from the data
• Model development for better customer engagement and retention
• Hands on experience in relevant tools like SQL(expert), Excel, R/Python
• Working on strategy development to increase business revenue

Requirements:

• Hands on experience in relevant tools like SQL(expert), Excel, R/Python
• Statistics: Strong knowledge of statistics
• Should able to do data scraping & Data mining
• Be self-driven, and show ability to deliver on ambiguous projects
• An ability and interest in working in a fast-paced, ambiguous and rapidly-changing environment
• Should have worked on Business Projects for an organization, Ex: customer acquisition, Customer retention.

We’re looking to hire someone to help scale Machine Learning and NLP efforts at Episource. You’ll work with the team that develops the models powering Episource’s product focused on NLP driven medical coding. Some of the problems include improving our ICD code recommendations , clinical named entity recognition and information extraction from clinical notes.

This is a role for highly technical machine learning & data engineers who combine outstanding oral and written communication skills, and the ability to code up prototypes and productionalize using a large range of tools, algorithms, and languages. Most importantly they need to have the ability to autonomously plan and organize their work assignments based on high-level team goals.

You will be responsible for setting an agenda to develop and ship machine learning models that positively impact the business, working with partners across the company including operations and engineering. You will use research results to shape strategy for the company, and help build a foundation of tools and practices used by quantitative staff across the company.

What you will achieve:

• Define the research vision for data science, and oversee planning, staffing, and prioritization to make sure the team is advancing that roadmap

• Invest in your team’s skills, tools, and processes to improve their velocity, including working with engineering counterparts to shape the roadmap for machine learning needs

• Hire, retain, and develop talented and diverse staff through ownership of our data science hiring processes, brand, and functional leadership of data scientists

• Evangelise machine learning and AI internally and externally, including attending conferences and being a thought leader in the space

• Partner with the executive team and other business leaders to deliver cross-functional research work and models

Required Skills:

• Strong background in classical machine learning and machine learning deployments is a must and preferably with 4-8 years of experience

• Knowledge of deep learning & NLP

• Hands-on experience in TensorFlow/PyTorch, Scikit-Learn, Python, Apache Spark & Big Data platforms to manipulate large-scale structured and unstructured datasets.

• Experience with GPU computing is a plus.

• Professional experience as a data science leader, setting the vision for how to most effectively use data in your organization. This could be through technical leadership with ownership over a research agenda, or developing a team as a personnel manager in a new area at a larger company.

• Expert-level experience with a wide range of quantitative methods that can be applied to business problems.

• Evidence you’ve successfully been able to scope, deliver and sell your own research in a way that shifts the agenda of a large organization.

• Excellent written and verbal communication skills on quantitative topics for a variety of audiences: product managers, designers, engineers, and business leaders.

• Fluent in data fundamentals: SQL, data manipulation using a procedural language, statistics, experimentation, and modeling

Qualifications

• Professional experience as a data science leader, setting the vision for how to most effectively use data in your organization

• Expert-level experience with machine learning that can be applied to business problems

• Evidence you’ve successfully been able to scope, deliver and sell your own work in a way that shifts the agenda of a large organization

• Fluent in data fundamentals: SQL, data manipulation using a procedural language, statistics, experimentation, and modeling

• Degree in a field that has very applicable use of data science / statistics techniques (e.g. statistics, applied math, computer science, OR a science field with direct statistics application)

• 5+ years of industry experience in data science and machine learning, preferably at a software product company

• 3+ years of experience managing data science teams, incl. managing/grooming managers beneath you

• 3+ years of experience partnering with executive staff on data topics