Sr Data Scientist - ML Algorithm, Statistical Learning, Python

UnitedHealth Group; Bengaluru; On-site; Confidential; Full time

Benefits offered by this job

Health benefits

Career development opportunities

Job summary

UnitedHealth Group’s Optum division seeks a seasoned Data Scientist to analyze large healthcare datasets, design and deploy ML/DL models, and develop production-grade solutions using Python and SQL. The role emphasizes advanced analytics, model governance, and collaboration with data engineering to deliver scalable insights.

You will work with TensorFlow, PyTorch, Keras, XGBoost, and NLP pipelines, contributing to generative and discriminative tasks while ensuring healthcare data literacy and

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field.
• 6+ years of experience in Data Scientist and relevant stream.
• Hands-on experience with AutoML platforms and deep learning frameworks.
• Solid understanding of statistics, mathematics, and data modeling techniques.
• Proficient in Python, R, and SQL.

Responsibilities

• Analyze large and complex datasets to extract actionable insights, applying solid statistical foundations, advanced analytics, and domain aware interpretation.
• Design, develop, and deploy machine learning and deep learning models across diverse business and healthcare use cases, ensuring rigor in feature engineering, model training, evaluation, and explainability.
• Build production grade ML solutions using robust, maintainable Python and SQL, with proper versioning, monitoring, performance governance, and scalable deployment patterns.
• Apply classical ML techniques (logistic regression, decision trees, clustering, PCA, Bayesian models, time series models) and advanced methods such as survival analysis and complex statistical modeling.
• Develop deep learning solutions using TensorFlow, PyTorch, Keras, or XGBoost for NLP, speech, image processing, and multimodal workloads; implement CNNs, RNNs, LSTMs/GRUs, and optimization/regularization techniques.
• Work with Hugging Face Transformers, embeddings, sequence models, and NLP/NLU pipelines to support generative and discriminative tasks.
• Build and optimize recommender systems using collaborative filtering, sequence aware models (FPMC, FISM, Fossil), and deep recommendation architectures.
• Explore and integrate graph machine learning techniques and knowledge graphs to model complex entity relationships, reasoning, and advanced analytics.
• Leverage AutoML tools (H2O.ai, Vertex/Google Cloud AutoML, DataRobot) to accelerate experimentation while maintaining scientific rigor and model quality.
• Apply healthcare data literacy to ensure compliant, domain aware model development, including familiarity with ICD, CPT, NDC, SNOMED, LOINC, FHIR, and HL7 datasets.
• Generate synthetic datasets using tools like Gretel.ai or Synthea to support experimentation where real data is limited or sensitive.
• Collaborate with data engineering teams to ensure high quality feature pipelines, correct transformations, and production ready integrations.
• Lead model governance practices, including documentation, model cards, validation reviews, responsible AI considerations, and continuous performance monitoring.
• Communicate insights, methodologies, experiment results, and model implications clearly to both technical and non technical stakeholders.
• Contribute to shared documentation, research notes, and knowledge artifacts using tools like Confluence.
• Stay updated with emerging research, GenAI/ML techniques, and industry trends; proactively bring forward innovations that enhance modeling capabilities.
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Skills

Python

R

SQL

Statistics

Problem solving

Communication

Education

Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or related field

Tools

AutoML platforms

TensorFlow

PyTorch

Keras

XGBoost

Hugging Face

Gretel.ai

Synthea

Job description

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.

Primary Responsibilities:

• Analyze large and complex datasets to extract actionable insights, applying solid statistical foundations, advanced analytics, and domain aware interpretation
• Design, develop, and deploy machine learning and deep learning models across diverse business and healthcare use cases, ensuring rigor in feature engineering, model training, evaluation, and explainability
• Build production grade ML solutions using robust, maintainable Python and SQL, with proper versioning, monitoring, performance governance, and scalable deployment patterns
• Apply classical ML techniques (logistic regression, decision trees, clustering, PCA, Bayesian models, time series models) and advanced methods such as survival analysis and complex statistical modeling
• Develop deep learning solutions using TensorFlow, PyTorch, Keras, or XGBoost for NLP, speech, image processing, and multimodal workloads; implement CNNs, RNNs, LSTMs/GRUs, and optimization/regularization techniques
• Work with Hugging Face Transformers, embeddings, sequence models, and NLP/NLU pipelines to support generative and discriminative tasks
• Build and optimize recommender systems using collaborative filtering, sequence aware models (FPMC, FISM, Fossil), and deep recommendation architectures
• Explore and integrate graph machine learning techniques and knowledge graphs to model complex entity relationships, reasoning, and advanced analytics
• Leverage AutoML tools (H2O.ai, Vertex/Google Cloud AutoML, DataRobot) to accelerate experimentation while maintaining scientific rigor and model quality
• Apply healthcare data literacy to ensure compliant, domain aware model development, including familiarity with ICD, CPT, NDC, SNOMED, LOINC, FHIR, and HL7 datasets
• Generate synthetic datasets using tools like Gretel.ai or Synthea to support experimentation where real data is limited or sensitive
• Collaborate with data engineering teams to ensure high quality feature pipelines, correct transformations, and production ready integrations
• Lead model governance practices, including documentation, model cards, validation reviews, responsible AI considerations, and continuous performance monitoring
• Communicate insights, methodologies, experiment results, and model implications clearly to both technical and non technical stakeholders
• Contribute to shared documentation, research notes, and knowledge artifacts using tools like Confluence
• Stay updated with emerging research, GenAI/ML techniques, and industry trends; proactively bring forward innovations that enhance modeling capabilities
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Required Qualifications:

• Bachelor\'s or Master\'s degree in Computer Science, Data Science, Statistics, or a related field
• 6+ years of experience in Data Scientist and relevant stream
• Hands-on experience with AutoML platforms and deep learning frameworks
• Solid understanding of statistics, mathematics, and data modeling techniques
• Familiarity with synthetic data generation tools and their applications
• Solid programming skills in Python, R, and SQL
• Proven excellent problem-solving skills and the ability to work independently or in a team
• Proven solid communication and documentation skills

At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.

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