Senior Data Scientist

Sr.Data Scientist,Python, AI ML

We are looking for a skilled Data Scientist to analyze complex datasets, develop predictive models, and generate actionable insights that support business decisions. The ideal candidate should have strong statistical, analytical, and programming skills, along with hands-on experience in machine learning.

Are you looking to work in the cutting edge area of applying data-science to help global customers get a better insight into their health? If so, read on and apply.

Role Name: Senior Data Scientist

Science Team | Full-Time | In-Office | Bangalore

The Role

The Ultrahuman Science Team builds the algorithms behind the Ring, M1 CGM, blood and urine biomarkers, and Performance Lab assessments. We are hiring a Senior Data Scientist to own those algorithms end to end: from the raw sensor signal to a model that is shipped, monitored, and trusted in users' hands.

This is a build role with real scope. In a typical month you will improve a production algorithm, root-cause a metric users are complaining about, and stand up the data pipeline the next model needs. The common thread is ownership: you take a vague question and return a working answer, without waiting to be handed scope.

What You'll Do

·      Own algorithms end to end: sleep staging, activity detection, sensor-derived metrics, and health scores. You frame the problem, build the features, train and evaluate the model, and see it live

·      Ship models, not notebooks: you prove a change on our own cohort before it reaches users, and a model is done only when it runs in production and you can tell how it is behaving

·      Validate against reference standards: design evaluations against gold standards, reference devices, and study ground truth, and know when a result is real and when it is an artifact

·      Own the data layer: cohort extraction, feature pipelines, study data, and raw sensor data, so the next model starts from clean inputs

What This Looks Like in Practice

1. Improving production algorithms - Take an existing production model like sleep staging, root-cause the failure modes against reference data, and ship a fix you can defend with numbers.

2. Building new models - Train an activity classifier on raw sensor data, design the labeled data collection that expands it, and pick the operating point so false positives never erode trust.

3. Proving it before it ships - Run a new steps algorithm against reference-device cohorts, decide with data when it is ready, and monitor how it behaves after rollout.

Who You Are

The two things we can't coach

·      High ownership, end to end: you take a problem from a vague question to a shipped model without waiting to be handed scope, and you can point to something you owned from raw data all the way to production

·      Hungry for more scope: you have outgrown your current role and want problems biggerthan your title, with the technical depth to be trusted with them

Also important

·      You've worked with human health data: wearables, physiological signals, or clinical data.

If your experience is close but not exact, show us why you will ramp fast

·      You've built at a startup: or somewhere small enough that nobody handed you clean data, clear specs, or a mature ML platform

·      You work like it's 2026: coding agents and AI tooling are part of how you build every day, and you can tell which new capabilities are worth adopting

·      You communicate: you can explain a model and its limits to a product manager, an engineer, or a founder, and hold your own with our scientists Core Technical Skills

·      Languages and data: Python and SQL daily, comfortable working in a real codebase

·      Machine learning: PyTorch or TensorFlow, scikit-learn, and gradient boosting, with the judgment to know which the problem needs

·      Advanced machine learning: time series and sequence models, deep learning on continuous physiological signals, and ensembles

·      Statistics and evaluation: hypothesis testing, experiment and A/B design, model evaluation, and error analysis against a reference standard

·      Scale and cloud: Spark or equivalent on large datasets, and AWS, GCP, or Azure

·      Production ML and MLOps: training pipelines, model versioning, deployment, monitoring, and drift detection

·      LLMs and agentic systems: fine-tuning and serving models, building agentic pipelines, and using coding agents to move faster

Experience:

- 4 to 5 years building and shipping machine learning systems. We index on what you have shipped and on trajectory, not the exact number of years; if you are a little earlier but have clearly outgrown your current scope, we want to hear from you.

- Bachelor's or higher in engineering, computer science, statistics, or a related field.

How We Work and Who Thrives Here

- The Science team is small and moves fast, and much of the work has no precedent to copy.

- People do their best work here when they are energized by ambiguity, low on ego, quick to adopt a better idea no matter where it comes from, and comfortable owning something before anyone has told them how. If you need a mature data org, clean labelled datasets, and clear guardrails to thrive, this particular role will not be the right fit, and that is worth knowing up front.

What You'll Gain

·      Ownership of algorithms that hundreds of thousands of people see every morning

·      A dataset most scientists never get to touch: 100M+ nights of sleep and continuous physiological signals at scale

·      Direct collaboration with the engineering, product, and design teams building Ultrahuman

We are looking for a talented and driven Data Scientist to join our growing Analytics team in India. In this role, you will work at the intersection of advanced machine learning, scalable MLOps infrastructure, and domain-specific healthcare analytics. You will collaborate closely with cross-functional teams to build, deploy, and maintain production-grade ML models that drive real-world impact in clinical trials and healthcare operations.

KEY RESPONSIBILITIES

End-to-End ML Development

•     Design, build, and optimize predictive models across the full ML lifecycle—from data ingestion to model serving.

•     Conduct rigorous Exploratory Data Analysis (EDA) to surface insights and drive feature engineering decisions.

•     Validate model performance using appropriate statistical techniques and domain knowledge.

MLOps & Production Deployment

•     Deploy, monitor, and maintain production-grade ML models using Databricks MLFlow endpoints and Unity Catalog.

•     Implement CI/CD pipelines for model versioning, experiment tracking, and automated retraining.

•     Ensure model reliability, observability, and performance in live production environments.

Language Models & LLM Applications

•     Apply transformer-based models (BERT, ClinicalBERT, Trial2Vec) for NLP tasks including classification, NER, and information extraction.

•     Build and maintain vector similarity search pipelines for semantic retrieval and recommendation use cases.

•     Fine-tune pre-trained models for domain-specific applications in clinical and healthcare contexts.

•     Support exploratory work around LLM integration and prompt engineering for internal tooling.

Domain-Driven Analytics

•     Apply advanced analytics within complex healthcare and clinical trial datasets—including patient records, trial protocols, and adverse event data.

•     Translate ambiguous business problems into structured analytical frameworks with measurable outcomes.

•     Partner with domain experts, product managers, and engineering teams to deliver data-driven solutions.

REQUIRED QUALIFICATIONS

Education

•     Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Bioinformatics, or a closely related field.

Experience

•     2–4 years of hands-on experience in a data science or machine learning role.

•     Demonstrable experience deploying ML models in production environments (not just prototyping).

Technical Skills

•     Strong proficiency in Python (pandas, NumPy, scikit-learn, PyTorch / TensorFlow).

•     Experience with Databricks, MLFlow (experiment tracking, model registry, endpoints), and Unity Catalog.

•     Hands-on experience with BERT-family models and Hugging Face Transformers library.

•     Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate) and embedding-based retrieval.

•     Solid understanding of SQL and working with large structured/unstructured datasets.

•     Exposure to cloud platforms (AWS / GCP / Azure) and distributed computing frameworks (Spark).

GOOD TO HAVE

•     Prior experience with clinical trial data standards (CDISC, CDASH, SDTM) or healthcare ontologies (SNOMED, ICD-10).

•     Familiarity with Trial2Vec or similar trial-to-vector embedding approaches.

•     Experience with LLM fine-tuning, RAG pipelines, or prompt engineering in a production setting.

•     Knowledge of regulatory and compliance considerations in healthcare AI (e.g., FDA guidelines, HIPAA).

•     Contributions to open-source ML projects or published research.

THIS ROLE IS NOT FOR YOU IF…

•     You have strong SQL/BI skills but limited hands-on ML modelling experience — or you’ve built models only in notebooks without ever deploying them to production.

•     Your LLM exposure is limited to API calls and prompt engineering — with no experience fine-tuning models, working with embeddings, or building vector search pipelines.

Description

We’re seeking a highly skilled, execution-focused Senior Data Scientist with a minimum of 5 years of experience. This role demands hands-on expertise in building, deploying, and optimizing machine learning models at scale, while working with big data technologies and modern cloud platforms. You will be responsible for driving data-driven solutions from experimentation to production, leveraging advanced tools and frameworks across Python, SQL, Spark, and AWS. The role requires strong technical depth, problem-solving ability, and ownership in delivering business impact through data science.

Responsibilities

• Design, build, and deploy scalable machine learning models into production systems.
• Develop advanced analytics and predictive models using Python, SQL, and popular ML/DL frameworks (Pandas, Scikit-learn, TensorFlow, PyTorch).
• Leverage Databricks, Apache Spark, and Hadoop for large-scale data processing and model training.
• Implement workflows and pipelines using Airflow and AWS EMR for automation and orchestration.
• Collaborate with engineering teams to integrate models into cloud-based applications on AWS.
• Optimize query performance, storage usage, and data pipelines for efficiency.
• Conduct end-to-end experiments, including data preprocessing, feature engineering, model training, validation, and deployment.
• Drive initiatives independently with high ownership and accountability.
• Stay up to date with industry best practices in machine learning, big data, and cloud-native deployments.

Requirements

• Minimum 5 years of experience in Data Science or Applied Machine Learning.
• Strong proficiency in Python, SQL, and ML libraries (Pandas, Scikit-learn, TensorFlow, PyTorch).
• Proven expertise in deploying ML models into production systems.
• Experience with big data platforms (Hadoop, Spark) and distributed data processing.
• Hands-on experience with Databricks, Airflow, and AWS EMR.
• Strong knowledge of AWS cloud services (S3, Lambda, SageMaker, EC2, etc.).
• Solid understanding of query optimization, storage systems, and data pipelines.
• Excellent problem-solving skills, with the ability to design scalable solutions.
• Strong communication and collaboration skills to work in cross-functional teams.

Benefits

• Best-in-class salary: We hire strong talent and compensate accordingly.
• Proximity Talks: Meet and learn from designers, engineers, product leaders, and AI practitioners.
• Continuous learning: Work with a world-class team and stay close to the latest in AI, engineering, and product development.
• High-impact work: Build AI-first systems and products used at scale by global clients.

About Us

Proximity is the trusted technology, design, and consulting partner for some of the biggest Sports, Media, and Entertainment companies in the world. We’re headquartered in San Francisco and have offices in Palo Alto, Dubai, Mumbai, and Bangalore.

Since 2019, Proximity has built high-impact, scalable products used by millions of users every day. Today, we are a global team of engineers, designers, product managers, and experts solving complex problems and building cutting-edge technology at scale.

🚀 We’re Hiring | Data Scientist 🧠📊

Ready to turn data into real-world intelligence? Join us and work on exciting AI/ML & data-driven solutions!

🔹 Experience: 8+ Years

🔹 Must-Have Skills:

🐍 Python | 🤖 Machine Learning | ☁️ Cloud | 🧠 NLP | 📊 Data Visualization

📍 Location: Pune

💼 Work Mode: Work from Office

If you're passionate about Data Science, AI & solving complex business problems, we’d love to hear from you!

📩 Interested? Kindly text

#Hiring #DataScientist #DataScience #MachineLearning #Python #NLP #AI #Cloud #DataVisualization #TechJobs #HiringNow

We are seeking a Senior Data Science & ML Associate with 4+ years of applied ML experience to build and ship models end-to-end from data prep and feature engineering to training, evaluation, and deployment driving measurable business impact.

Key Responsibilities

• Build, train, and evaluate ML and deep-learning models

• Engineer features and prepare data at scale

• Deploy models and monitor production performance

• Partner with stakeholders to frame problems and metrics

• Communicate results and drive decisions

• Iterate on models from business feedback

Mandatory Skills

• 4+ years applied machine learning

• Strong Python (Pandas, NumPy, scikit-learn)

• Classical ML and deep learning (TensorFlow/PyTorch)

• Solid statistics and experiment design

• SQL and data wrangling at scale

• Model deployment / MLOps exposure

Nice to Have: NLP or computer vision; cloud ML (SageMaker, Azure ML)

Role Overview 

As a Data Scientist, you will work with business stakeholders, AI engineers, and domain experts to transform data into actionable insights and intelligent solutions. You will develop machine learning models, perform statistical analysis, and contribute to AI-driven products that create measurable business impact. 

Key Responsibilities 

Data Science & Machine Learning 

• Analyze structured and unstructured data to identify patterns, trends, and business opportunities.  
• Perform exploratory data analysis (EDA), feature engineering, and data preparation.  
• Develop, evaluate, and optimize machine learning models for prediction, classification, clustering, and forecasting.  
• Apply statistical techniques to solve business problems and validate model performance.  
• Design and execute experiments to improve model accuracy and business outcomes.  

 AI Solution Development 

• Collaborate with AI Engineers, Data Engineers, and domain experts to build AI-powered solutions.  
• Translate business requirements into scalable data science approaches.  
• Contribute to Generative AI and advanced analytics initiatives where applicable.  
• Document methodologies, model performance, and key findings.  

 Required Technical Skills 

• Strong programming skills in Python and SQL for data analysis, feature engineering, and machine learning.  
• Strong understanding of Statistics, Probability, Linear Algebra, and Calculus as applied to machine learning and data science.  
• Experience with Exploratory Data Analysis (EDA), data preprocessing, feature engineering, feature selection, and handling missing or imbalanced data.  
• Good understanding of Supervised, Unsupervised, and Ensemble Machine Learning algorithms, including their assumptions, strengths, limitations, and appropriate use cases.  
• Strong knowledge of Regression, Classification, Clustering, Time Series Forecasting, Dimensionality Reduction, Recommendation Systems, and Anomaly Detection techniques.  
• Experience with Model Evaluation, Cross-Validation, Hyperparameter Optimization, Bias-Variance Trade-off, Feature Importance, Explainable AI (XAI), and Performance Metrics.  
• Understanding of Statistical Inference, Hypothesis Testing, Probability Distributions, Sampling Techniques, Confidence Intervals, and A/B Testing.  
• Experience translating business problems into analytical approaches and developing scalable, data-driven solutions.  
• Working knowledge of Generative AI, Large Language Models (LLMs), Prompt Engineering, and Retrieval-Augmented Generation (RAG) is preferred. 

Preferred Qualifications 

• Bachelor's or master's degree in computer science, Artificial Intelligence, Data Science, Statistics, Mathematics, Engineering, or a related field.  
• 2–4 years of experience developing machine learning or data science solutions.  
• Experience working on end-to-end data science projects in a business environment.  

 Nice to Have 

• Exposure to Generative AI, LLMs, RAG, or Agentic AI.  
• Experience with Computer Vision or Natural Language Processing (NLP).  
• Familiarity with cloud-based AI platforms.  
• Knowledge of construction, engineering, manufacturing, or industrial domains.  
• Participation in hackathons, research, Kaggle competitions, or open-source projects.  

 Soft Skills 

Strong analytical and problem-solving skills, effective communication and collaboration, ownership mindset, adaptability, continuous learning, and a passion for innovation.

Hiring for Data Scientist / Senior Data Scientist

Exp : 4 - 12 yrs

Edu : BE/B.tech/MCA

Work Location : Pune

Notice Period : Immediate - 15 days

Skills :

4+ years of experience in data engineering, data science, or related domains.

Hands-on experience with SQL, Python, and distributed data systems.

Knowledge of machine learning techniques and statistical analysis.

Experience with cloud data platforms (Azure Data Factory, AWS Glue, GCP BigQuery).

Familiarity with DevOps practices and CI/CD for data pipelines.

Platforms & Operations Experience (Preferred)

- Experience working with Azure, AWS, or Google Cloud data tools.

Operational experience with data orchestration tools (Airflow, ADF, Glue).

Understanding of Kubernetes, Docker, or containerized environments.

Hands-on experience with data warehousing platforms (Snowflake, Redshift, BigQuery).

Experience in monitoring, logging, and alerting operations for data workflows.

We are looking for a talented and driven Data Scientist to join our growing Analytics team in India. In this role, you will work at the intersection of advanced machine learning, scalable MLOps infrastructure, and domain-specific healthcare analytics. You will collaborate closely with cross-functional teams to build, deploy, and maintain production-grade ML models that drive real-world impact in clinical trials and healthcare operations.

KEY RESPONSIBILITIES

End-to-End ML Development

•     Design, build, and optimize predictive models across the full ML lifecycle—from data ingestion to model serving.

•     Conduct rigorous Exploratory Data Analysis (EDA) to surface insights and drive feature engineering decisions.

•     Validate model performance using appropriate statistical techniques and domain knowledge.

MLOps & Production Deployment

•     Deploy, monitor, and maintain production-grade ML models using Databricks MLFlow endpoints and Unity Catalog.

•     Implement CI/CD pipelines for model versioning, experiment tracking, and automated retraining.

•     Ensure model reliability, observability, and performance in live production environments.

Language Models & LLM Applications

•     Apply transformer-based models (BERT, ClinicalBERT, Trial2Vec) for NLP tasks including classification, NER, and information extraction.

•     Build and maintain vector similarity search pipelines for semantic retrieval and recommendation use cases.

•     Fine-tune pre-trained models for domain-specific applications in clinical and healthcare contexts.

•     Support exploratory work around LLM integration and prompt engineering for internal tooling.

Domain-Driven Analytics

•     Apply advanced analytics within complex healthcare and clinical trial datasets—including patient records, trial protocols, and adverse event data.

•     Translate ambiguous business problems into structured analytical frameworks with measurable outcomes.

•     Partner with domain experts, product managers, and engineering teams to deliver data-driven solutions.

REQUIRED QUALIFICATIONS

Education

•     Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Bioinformatics, or a closely related field.

Experience

•     2–4 years of hands-on experience in a data science or machine learning role.

•     Demonstrable experience deploying ML models in production environments (not just prototyping).

Technical Skills

•     Strong proficiency in Python (pandas, NumPy, scikit-learn, PyTorch / TensorFlow).

•     Experience with Databricks, MLFlow (experiment tracking, model registry, endpoints), and Unity Catalog.

•     Hands-on experience with BERT-family models and Hugging Face Transformers library.

•     Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate) and embedding-based retrieval.

•     Solid understanding of SQL and working with large structured/unstructured datasets.

•     Exposure to cloud platforms (AWS / GCP / Azure) and distributed computing frameworks (Spark).

GOOD TO HAVE

•     Prior experience with clinical trial data standards (CDISC, CDASH, SDTM) or healthcare ontologies (SNOMED, ICD-10).

•     Familiarity with Trial2Vec or similar trial-to-vector embedding approaches.

•     Experience with LLM fine-tuning, RAG pipelines, or prompt engineering in a production setting.

•     Knowledge of regulatory and compliance considerations in healthcare AI (e.g., FDA guidelines, HIPAA).

•     Contributions to open-source ML projects or published research.

THIS ROLE IS NOT FOR YOU IF…

•     You have strong SQL/BI skills but limited hands-on ML modelling experience — or you’ve built models only in notebooks without ever deploying them to production.

•     Your LLM exposure is limited to API calls and prompt engineering — with no experience fine-tuning models, working with embeddings, or building vector search pipelines.

The Role

Own end-to-end credit & fraud data science: feature engineering from raw bureau JSON ,SMS,DEVICE, scorecard / model development, Business Rule Engine (BRE) design, monitoring, and partnering with product/engineering to put rules live. You will work directly with the existing DS team,Tech,product and founders — decisions are data-backed and debated.

What you will own

1. Build and maintain credit scorecards and models for FTB and Repeat Borrowers (Xgboost, Random forest, Support Vector Machine Models, ensemble models, challenger models).
2. Engineer features from raw CRIF (or equivalent) bureau JSON — tradelines, enquiries, DPD histories, identity matches — and from raw SMS / FinBox alt-data (collections, rejections, salary, app footprint).
3. Design, validate, and ship Models: hard rejects, soft flags, amount caps — with clear lift/capture

/ approval trade-offs.

1. Own portfolio risk analytics: vintage / DPD / non-starter / POS bad-rate monitoring; propose tier pauses, cool-offs, and ladder-up changes.
2. Build fraud signals (device, SIM/OTP, mule, ring, post-disbursal disappearance) and help prioritise the fraud PRD backlog into production.
3. Partner with engineering to productionise features, rules, and models (Watchtower-style shadow underwriting, policy index, monitoring dashboards).
4. Challenge and refine existing tier/ladder policy with evidence; communicate clearly to founders and business.

Required experience

• Tenure: 5+ years overall experience in data science/analytics.
• Digital lending: Minimum 3 years hands-on in digital lending/consumer credit (NBFC, fintech lender, digital/STPL/) who has built models themselves.
• Scorecards/models: Built and deployed at least one credit scorecard (first-time borrower or repeat borrower, or combined model) into a live BRE / LOS. Should improve approval–bad-rate trade-offs from production experience.
• Bureau: Parsed and engineered features from raw bureau files (CRIF / CIBIL / Experian JSON or XML) — not only vendor-precomputed attributes.
• Non-starter models: Fraud/non-starter / First Payment default modelling experience in short-tenure lending.
• Limit Assignment: Experience with repeat-borrower ladder / limit-management policies.
• Monitoring and QC: Shadow underwriting/champion–challenger frameworks.
• Alt-data: Worked with SMS / alt-data / device / AA signals for underwriting or fraud (FinBox, similar vendors, or in-house SMS parsing).
• Stack: Strong SQL + Python (pandas, sklearn/Logistic / lightgbm/Xgboost/randomforest, statsmodels). Able to write production-quality notebooks and scripts, not just slide decks.
• Communication: Comfortable debating policy with founders/credit heads using data; owns the "show me the evidence" conversation.

Nice to have:

• Feature stores, Airflow/cron pipelines, S3 + Postgres + DynamoDB.
• Prior Experience: Prior work at a zero-to-one digital lender or STPL product.

What success looks like in 6 months

• A documented feature dictionary from raw bureau + SMS with IV/KS ranking.
• At least one new scorecard/model live with clear expected vs observed bad-rate impact.
• Non-starter / First Payment Defaults monitoring with actionable rule recommendations and clear demonstrated improvements in defaults
• Credible pushback on weak policy ideas — backed by analysis, not opinion.