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
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Bachelor’s degree or greater in Computer Science, IT or related fields
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Minimum of 5 years of experience in cloud, DevOps, MLOps & data projects
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Strong experience with bash scripting, unix environments and building scalable/distributed systems
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Experience with automation/configuration management using Ansible, Terraform, or equivalent
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Very strong experience with AWS and Python
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Experience building CI/CD systems
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Experience with containerization technologies like Docker, Kubernetes, ECS, EKS or equivalent
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Ability to build and manage application and performance monitoring processes
About Episource LLC
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Roles & Responsibilities:
Work on implementation of real-time and batch data pipelines for disparate data sources.
- Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS technologies.
- Build and maintain an analytics layer that utilizes the underlying data to generate dashboards and provide actionable insights.
- Identify improvement areas in the current data system and implement optimizations.
- Work on specific areas of data governance including metadata management and data quality management.
- Participate in discussions with Product Management and Business stakeholders to understand functional requirements and interact with other cross-functional teams as needed to develop, test, and release features.
- Develop Proof-of-Concepts to validate new technology solutions or advancements.
- Work in an Agile Scrum team and help with planning, scoping and creation of technical solutions for the new product capabilities, through to continuous delivery to production.
- Work on building intelligent systems using various AI/ML algorithms.
Desired Experience/Skill:
- Must have worked on Analytics Applications involving Data Lakes, Data Warehouses and Reporting Implementations.
- Experience with private and public cloud architectures with pros/cons.
- Ability to write robust code in Python and SQL for data processing. Experience in libraries such as Pandas is a must; knowledge of one of the frameworks such as Django or Flask is a plus.
- Experience in implementing data processing pipelines using AWS services: Kinesis, Lambda, Redshift/Snowflake, RDS.
- Knowledge of Kafka, Redis is preferred
- Experience on design and implementation of real-time and batch pipelines. Knowledge of Airflow is preferred.
- Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)
Your Day-to-Day
- Assist our Growth Strategists in analyzing the results of A/B experiments.
- Analysis on customer engagement rates, customer acquisition projects ( ex. Churn rate prediction, Attribution etc. )
- Analyse marketing channel performance and Deep-dive reports to stakeholders and Management
- Building statistical experimentation templates for faster A/B outputs
- Work on forecasting models and assisting senior Management in creating frameworks on growth models
- Local implementation of marketing analytics projects related to improving marketing channels effectiveness, customer segmentation, campaign optimization, etc
- Monitor campaigns against key performance indicators (KPIs), be fully aware of trends and analytics, success and risks in order to achieve business objectives.
- Communicate complex ideas into understandable reports/documentation and this will include leveraging on leading software tools such as Tableau.
Your Know-Know
- 3-5 years of experience in strategy / consulting / analytical / project management roles; experience in e-commerce, Start-ups or Unicorns(CARS24,OLA,SWIGGY,FLIPKART,OYO) or entrepreneur experience preferred + At Least 2 years of experience leading a team
- Top-notch academics from a Tier 1 college (IIM / IIT/ NIT)
- Must have SQL/PostgreSQL/Tableau Experience.
- Added advantage: Experience with Google Analytics, CRM (MoEngage, Braze,Leanplum).
- Preferred knowledge of statistical computer languages (Python / R, etc).
- Analytical mindset with ability to present data in a structured and informative way
- Enjoy a fast-paced environment and can align business objectives with product priorities
Responsibilities: - Write and maintain production level code in Python for deploying machine learning models - Create and maintain deployment pipelines through CI/CD tools (preferribly GitLab CI) - Implement alerts and monitoring for prediction accuracy and data drift detection - Implement automated pipelines for training and replacing models - Work closely with with the data science team to deploy new models to production Required Qualifications: - Degree in Computer Science, Data Science, IT or a related discipline. - 2+ years of experience in software engineering or data engineering. - Programming experience in Python - Experience in data profiling, ETL development, testing and implementation - Experience in deploying machine learning models
Good to have: - Experience in AWS resources for ML and data engineering (SageMaker, Glue, Athena, Redshift, S3) - Experience in deploying TensorFlow models - Experience in deploying and managing ML Flow
Knowledge of Hadoop ecosystem installation, initial-configuration and performance tuning.
Expert with Apache Ambari, Spark, Unix Shell scripting, Kubernetes and Docker
Knowledge on python would be desirable.
Experience with HDP Manager/clients and various dashboards.
Understanding on Hadoop Security (Kerberos, Ranger and Knox) and encryption and Data masking.
Experience with automation/configuration management using Chef, Ansible or an equivalent.
Strong experience with any Linux distribution.
Basic understanding of network technologies, CPU, memory and storage.
Database administration a plus.
Qualifications and Education Requirements
2 to 4 years of experience with and detailed knowledge of Core Hadoop Components solutions and
dashboards running on Big Data technologies such as Hadoop/Spark.
Bachelor degree or equivalent in Computer Science or Information Technology or related fields.
Qualifications
B.Tech/M.Tech
Percentage 70% and above
2018 & 2019 passouts
At least 3 POC Implementations should have done
Premium Institutes passouts are more preferrable
What you’ll do
- Deliver plugins for our Python-based ETL pipelines.
- Deliver Python microservices for provisioning and managing cloud infrastructure.
- Implement algorithms to analyse large data sets.
- Draft design documents that translate requirements into code.
- Deal with challenges associated with handling large volumes of data.
- Assume responsibilities from technical design through technical client support.
- Manage expectations with internal stakeholders and context-switch in a fast paced environment.
- Thrive in an environment that uses AWS and Elasticsearch extensively.
- Keep abreast of technology and contribute to the engineering strategy.
- Champion best development practices and provide mentorship.
What we’re looking for
- Experience in Python 3.
- Python libraries used for data (such as pandas, numpy).
- AWS.
- Elasticsearch.
- Performance tuning.
- Object Oriented Design and Modelling.
- Delivering complex software, ideally in a FinTech setting.
- CI/CD tools.
- Knowledge of design patterns.
- Sharp analytical and problem-solving skills.
- Strong sense of ownership.
- Demonstrable desire to learn and grow.
- Excellent written and oral communication skills.
- Mature collaboration and mentoring abilities.
About SteelEye Culture
- Work from home until you are vaccinated against COVID-19
- Top of the line health insurance • Order discounted meals every day from a dedicated portal
- Fair and simple salary structure
- 30+ holidays in a year
- Fresh fruits every day
- Centrally located. 5 mins to the nearest metro station (MG Road)
- Measured on output and not input
We are looking for an outstanding Big Data Engineer with experience setting up and maintaining Data Warehouse and Data Lakes for an Organization. This role would closely collaborate with the Data Science team and assist the team build and deploy machine learning and deep learning models on big data analytics platforms.
Roles and Responsibilities:
- Develop and maintain scalable data pipelines and build out new integrations and processes required for optimal extraction, transformation, and loading of data from a wide variety of data sources using 'Big Data' technologies.
- Develop programs in Scala and Python as part of data cleaning and processing.
- Assemble large, complex data sets that meet functional / non-functional business requirements and fostering data-driven decision making across the organization.
- Responsible to design and develop distributed, high volume, high velocity multi-threaded event processing systems.
- Implement processes and systems to validate data, monitor data quality, ensuring production data is always accurate and available for key stakeholders and business processes that depend on it.
- Perform root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
- Provide high operational excellence guaranteeing high availability and platform stability.
- Closely collaborate with the Data Science team and assist the team build and deploy machine learning and deep learning models on big data analytics platforms.
Skills:
- Experience with Big Data pipeline, Big Data analytics, Data warehousing.
- Experience with SQL/No-SQL, schema design and dimensional data modeling.
- Strong understanding of Hadoop Architecture, HDFS ecosystem and eexperience with Big Data technology stack such as HBase, Hadoop, Hive, MapReduce.
- Experience in designing systems that process structured as well as unstructured data at large scale.
- Experience in AWS/Spark/Java/Scala/Python development.
- Should have Strong skills in PySpark (Python & SPARK). Ability to create, manage and manipulate Spark Dataframes. Expertise in Spark query tuning and performance optimization.
- Experience in developing efficient software code/frameworks for multiple use cases leveraging Python and big data technologies.
- Prior exposure to streaming data sources such as Kafka.
- Should have knowledge on Shell Scripting and Python scripting.
- High proficiency in database skills (e.g., Complex SQL), for data preparation, cleaning, and data wrangling/munging, with the ability to write advanced queries and create stored procedures.
- Experience with NoSQL databases such as Cassandra / MongoDB.
- Solid experience in all phases of Software Development Lifecycle - plan, design, develop, test, release, maintain and support, decommission.
- Experience with DevOps tools (GitHub, Travis CI, and JIRA) and methodologies (Lean, Agile, Scrum, Test Driven Development).
- Experience building and deploying applications on on-premise and cloud-based infrastructure.
- Having a good understanding of machine learning landscape and concepts.
Qualifications and Experience:
Engineering and post graduate candidates, preferably in Computer Science, from premier institutions with proven work experience as a Big Data Engineer or a similar role for 3-5 years.
Certifications:
Good to have at least one of the Certifications listed here:
AZ 900 - Azure Fundamentals
DP 200, DP 201, DP 203, AZ 204 - Data Engineering
AZ 400 - Devops Certification
- Should have good hands-on experience in Informatica MDM Customer 360, Data Integration(ETL) using PowerCenter, Data Quality.
- Must have strong skills in Data Analysis, Data Mapping for ETL processes, and Data Modeling.
- Experience with the SIF framework including real-time integration
- Should have experience in building C360 Insights using Informatica
- Should have good experience in creating performant design using Mapplets, Mappings, Workflows for Data Quality(cleansing), ETL.
- Should have experience in building different data warehouse architecture like Enterprise,
- Federated, and Multi-Tier architecture.
- Should have experience in configuring Informatica Data Director in reference to the Data
- Governance of users, IT Managers, and Data Stewards.
- Should have good knowledge in developing complex PL/SQL queries.
- Should have working experience on UNIX and shell scripting to run the Informatica workflows and to control the ETL flow.
- Should know about Informatica Server installation and knowledge on the Administration console.
- Working experience with Developer with Administration is added knowledge.
- Working experience in Amazon Web Services (AWS) is an added advantage. Particularly on AWS S3, Data pipeline, Lambda, Kinesis, DynamoDB, and EMR.
- Should be responsible for the creation of automated BI solutions, including requirements, design,development, testing, and deployment
The candidate must have Expertise in ADF(Azure data factory), well versed with python.
Performance optimization of scripts (code) and Productionizing of code (SQL, Pandas, Python or PySpark, etc.)
Required skills:
Bachelors in - in Computer Science, Data Science, Computer Engineering, IT or equivalent
Fluency in Python (Pandas), PySpark, SQL, or similar
Azure data factory experience (min 12 months)
Able to write efficient code using traditional, OO concepts, modular programming following the SDLC process.
Experience in production optimization and end-to-end performance tracing (technical root cause analysis)
Ability to work independently with demonstrated experience in project or program management
Azure experience ability to translate data scientist code in Python and make it efficient (production) for cloud deployment
Mining large volumes of credit behavior data to generate insights around product holdings and monetization opportunities for cross sell
Use data science to size opportunity and product potential for launch of any new product/pilots
Build propensity models using heuristics and campaign performance to maximize efficiency.
Conduct portfolio analysis and establish key metrics for cross sell partnership
Desired profile/Skills:
2-5 years of experience with a degree in any quantitative discipline such as Engineering, Computer Science, Economics, Statistics or Mathematics
Excellent problem solving and comprehensive analytical skills – ability to structure ambiguous problem statements, perform detailed analysis and derive crisp insights.
Solid experience in using python and SQL
Prior work experience in a financial services space would be highly valued
Location: Bangalore/ Ahmedabad
