Data Scientist

Job Description:

1.Be a hands on problem solver with consultative approach, who can apply Machine Learning & Deep Learning algorithms to solve business challenges

a. Use the knowledge of wide variety of AI/ML techniques and algorithms to find what combinations of these techniques can best solve the problem

b. Improve Model accuracy to deliver greater business impact

c.Estimate business impact due to deployment of model

2.Work with the domain/customer teams to understand business context , data dictionaries and apply relevant Deep Learning solution for the given business challenge

3.Working with tools and scripts for sufficiently pre-processing the data & feature engineering for model development – Python / R / SQL / Cloud data pipelines

4.Design , develop & deploy Deep learning models using Tensorflow / Pytorch 

5.Experience in using Deep learning models with text, speech, image and video data

a.Design & Develop NLP models for Text Classification, Custom Entity Recognition, Relationship extraction, Text Summarization, Topic Modeling, Reasoning over Knowledge Graphs, Semantic Search using NLP tools like Spacy and opensource Tensorflow, Pytorch, etc

b.Design and develop Image recognition & video analysis models using Deep learning algorithms and open source tools like OpenCV

c.Knowledge of State of the art Deep learning algorithms

6.Optimize and tune Deep Learnings model for best possible accuracy

7.Use visualization tools/modules to be able to explore and analyze outcomes & for Model validation eg: using Power BI / Tableau

8.Work with application teams, in deploying models on cloud as a service or on-prem

a.Deployment of models in Test / Control framework for tracking

b.Build CI/CD pipelines for ML model deployment

9.Integrating AI&ML models with other applications using REST APIs and other connector technologies

10.Constantly upskill and update with the latest techniques and best practices. Write white papers and create demonstrable assets to summarize the AIML work and its impact.

· Technology/Subject Matter Expertise

• Sufficient expertise in machine learning, mathematical and statistical sciences
• Use of versioning & Collaborative tools like Git / Github
• Good understanding of landscape of AI solutions – cloud, GPU based compute, data security and privacy, API gateways, microservices based architecture, big data ingestion, storage and processing, CUDA Programming
• Develop prototype level ideas into a solution that can scale to industrial grade strength
• Ability to quantify & estimate the impact of ML models.

· Softskills Profile

• Curiosity to think in fresh and unique ways with the intent of breaking new ground.
• Must have the ability to share, explain and “sell” their thoughts, processes, ideas and opinions, even outside their own span of control
• Ability to think ahead, and anticipate the needs for solving the problem will be important

·        Ability to communicate key messages effectively, and articulate strong opinions in large forums

· Desirable Experience:

• Keen contributor to open source communities, and communities like Kaggle
• Ability to process Huge amount of Data using Pyspark/Hadoop
• Development & Application of Reinforcement Learning
• Knowledge of Optimization/Genetic Algorithms
• Operationalizing Deep learning model for a customer and understanding nuances of scaling such models in real scenarios
• Optimize and tune deep learning model for best possible accuracy
• Understanding of stream data processing, RPA, edge computing, AR/VR etc
• Appreciation of digital ethics, data privacy will be important
• Experience of working with AI & Cognitive services platforms like Azure ML, IBM Watson, AWS Sagemaker, Google Cloud will all be a big plus
• Experience in platforms like Data robot, Cognitive scale, H2O.AI etc will all be a big plus

Requirements:

● Understanding our data sets and how to bring them together.

● Working with our engineering team to support custom solutions offered to the product development.

● Filling the gap between development, engineering and data ops.

● Creating, maintaining and documenting scripts to support ongoing custom solutions.

● Excellent organizational skills, including attention to precise details

● Strong multitasking skills and ability to work in a fast-paced environment

● 5+ years experience with Python to develop scripts.

● Know your way around RESTFUL APIs.[Able to integrate not necessary to publish]

● You are familiar with pulling and pushing files from SFTP and AWS S3.

● Experience with any Cloud solutions including GCP / AWS / OCI / Azure.

● Familiarity with SQL programming to query and transform data from relational Databases.

● Familiarity to work with Linux (and Linux work environment).

● Excellent written and verbal communication skills

● Extracting, transforming, and loading data into internal databases and Hadoop

● Optimizing our new and existing data pipelines for speed and reliability

● Deploying product build and product improvements

● Documenting and managing multiple repositories of code

● Experience with SQL and NoSQL databases (Casendra, MySQL)

● Hands-on experience in data pipelining and ETL. (Any of these frameworks/tools: Hadoop, BigQuery,

RedShift, Athena)

● Hands-on experience in AirFlow

● Understanding of best practices, common coding patterns and good practices around

● storing, partitioning, warehousing and indexing of data

● Experience in reading the data from Kafka topic (both live stream and offline)

● Experience in PySpark and Data frames

Responsibilities:

You’ll

● Collaborating across an agile team to continuously design, iterate, and develop big data systems.

● Extracting, transforming, and loading data into internal databases.

● Optimizing our new and existing data pipelines for speed and reliability.

● Deploying new products and product improvements.

● Documenting and managing multiple repositories of code.

Requirements

Experience

• 5+ years of professional experience in implementing MLOps framework to scale up ML in production.
• Hands-on experience with Kubernetes, Kubeflow, MLflow, Sagemaker, and other ML model experiment management tools including training, inference, and evaluation.
• Experience in ML model serving (TorchServe, TensorFlow Serving, NVIDIA Triton inference server, etc.)
• Proficiency with ML model training frameworks (PyTorch, Pytorch Lightning, Tensorflow, etc.).
• Experience with GPU computing to do data and model training parallelism.
• Solid software engineering skills in developing systems for production.
• Strong expertise in Python.
• Building end-to-end data systems as an ML Engineer, Platform Engineer, or equivalent.
• Experience working with cloud data processing technologies (S3, ECR, Lambda, AWS, Spark, Dask, ElasticSearch, Presto, SQL, etc.).
• Having Geospatial / Remote sensing experience is a plus.

Objective

Data Engineer will be responsible for expanding and optimizing our data and database architecture, as well as optimizing data flow and collection for cross functional teams. The ideal candidate is an experienced data pipeline builder and data wrangler who enjoys optimizing data systems and building. The Data Engineer will support our software developers, database architects, data analysts and data scientists on data initiatives and will ensure optimal data delivery architecture is consistent throughout ongoing projects. They must be self-directed and comfortable supporting the data needs of multiple teams, systems, and products

Roles and Responsibilities:

• Should be comfortable in building and optimizing performant data pipelines which include data ingestion, data cleansing and curation into a data warehouse, database, or any other data platform using DASK/Spark.
• Experience in distributed computing environment and Spark/DASK architecture.
• Optimize performance for data access requirements by choosing the appropriate file formats (AVRO, Parquet, ORC etc) and compression codec respectively.
• Experience in writing production ready code in Python and test, participate in code reviews to maintain and improve code quality, stability, and supportability.
• Experience in designing data warehouse/data mart.
• Experience with any RDBMS preferably SQL Server and must be able to write complex SQL queries.
• Expertise in requirement gathering, technical design and functional documents.
• Experience in Agile/Scrum practices.
• Experience in leading other developers and guiding them technically.
• Experience in deploying data pipelines using automated CI/CD approach.
• Ability to write modularized reusable code components.
• Proficient in identifying data issues and anomalies during analysis.
• Strong analytical and logical skills.
• Must be able to comfortably tackle new challenges and learn.
• Must have strong verbal and written communication skills.

Required skills:

• Knowledge on GCP
• Expertise in Google BigQuery
• Expertise in Airflow
• Good Hands on SQL
• Data warehousing concepts

The role involves computer vision tasks including development, customization and training of Convolutional Neural Networks (CNNs); application of ML techniques (SVM, regression, clustering etc. ) and traditional Image Processing (OpenCV etc. ). The role is research focused and would involve going through and implementing existing research papers, deep dive of problem analysis, generating new ideas, automating and optimizing key processes.

Requirements:

• 2 - 4 years of relevant experience in solving complex real-world problems at scale via deep learning, computer vision or AI
• Python, cuDNN, Tensorflow/PyTorch/Keras (or similar Deep Learning frameworks).
• CNNs, RNNs, Transfer learning (for image classification, segmentation, object detection etc).
• Image Processing techniques using OpenCV or other white-box image feature extraction algorithms.
• End to end deployment of deep learning models.

Responsibilities Description:

Responsible for the development and implementation of machine learning algorithms and techniques to solve business problems and optimize member experiences. Primary duties may include are but not limited to: Design machine learning projects to address specific business problems determined by consultation with business partners. Work with data-sets of varying degrees of size and complexity including both structured and unstructured data. Piping and processing massive data-streams in distributed computing environments such as Hadoop to facilitate analysis. Implements batch and real-time model scoring to drive actions. Develops machine learning algorithms to build customized solutions that go beyond standard industry tools and lead to innovative solutions. Develop sophisticated visualization of analysis output for business users.

Experience Requirements:

BS/MA/MS/PhD in Statistics, Computer Science, Mathematics, Machine Learning, Econometrics, Physics, Biostatistics or related Quantitative disciplines. 2-4 years of experience in predictive analytics and advanced expertise with software such as Python, or any combination of education and experience which would provide an equivalent background. Experience in the healthcare sector. Experience in Deep Learning strongly preferred.

Required Technical Skill Set:

• Full cycle of building machine learning solutions,

o   Understanding of wide range of algorithms and their corresponding problems to solve

o   Data preparation and analysis

o   Model training and validation

o   Model application to the problem

• Experience using the full open source programming tools and utilities
• Experience in working in end-to-end data science project implementation.
• 2+ years of experience with development and deployment of Machine Learning applications
• 2+ years of experience with NLP approaches in a production setting
• Experience in building models using bagging and boosting algorithms
• Exposure/experience in building Deep Learning models for NLP/Computer Vision use cases preferred
• Ability to write efficient code with good understanding of core Data Structures/algorithms is critical
• Strong python skills following software engineering best practices
• Experience in using code versioning tools like GIT, bit bucket
• Experience in working in Agile projects
• Comfort & familiarity with SQL and Hadoop ecosystem of tools including spark
• Experience managing big data with efficient query program good to have
• Good to have experience in training ML models in tools like Sage Maker, Kubeflow etc.
• Good to have experience in frameworks to depict interpretability of models using libraries like Lime, Shap etc.
• Experience with Health care sector is preferred
• MS/M.Tech or PhD is a plus

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