Senior Data Scientist (Digital Lending)

Strong Lead Data Science, / AI Engineer / Machine Learning Engineer profiles.

2

Mandatory (Experience 1) - Must have 10+ years of experience in Data Science, AI/ML or AI Engineering with hands-on experience building production-grade ML systems.

3

Mandatory (Experience 2) - Must have hands-on experience building AI/ML solutions for Credit Risk, Fraud Risk Management (FRM), Collections & Recovery, with proven delivery of business-impacting AI/ML solutions.

4

Mandatory (Experience 3) - Candidate's Current designation must be Lead or above.

5

Mandatory (Experience 4) - Must have strong experience designing and deploying large-scale distributed Machine Learning systems, including model training, fine-tuning, inference, scalable serving, and production deployment.

6

Mandatory (Experience 5) - Strong programming experience in Python, along with exposure to Spark, Kafka, Kubernetes, APIs/Microservices, CI/CD, Feature Store, Model Registry, and Distributed Computing.

7

Mandatory (Experience 6) - Experience designing and deploying Credit Risk Models, Fraud Detection Models, Graph ML, Early Warning Systems, Portfolio Monitoring, Collections Optimization, Propensity Models, and Recovery Forecasting.

8

Mandatory (Experience 7) - Proven experience leading AI/ML teams, owning end-to-end delivery, mentoring engineers, driving cross-functional execution, and managing production AI platforms.

9

Mandatory (Experience 8) – Must have experience working under BFSI governance, including PII handling, auditability, model governance, compliance, secure-by-design architecture, approval workflows, and model risk management practices.

10

Mandatory ( Education ) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered

11

Mandatory (Age) - Candidate's Age should be below 37 years.

12

Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

13

Preferred (Experience 1) - Candidates currently working as Lead / Principal / Engineering Manager / Associate Director / Director in reputed Product, FinTech, Banking, NBFC, or Global Capability Centers will be preferred.

14

Preferred (Experience 2) - Indian professionals currently working overseas (NRI) who are planning to relocate and permanently settle in India are encouraged to apply.

15

Preferred (Experience 4) - Experience building enterprise AI platforms using Graph ML, Vector Databases, LLM-enabled decisioning, distributed training frameworks, and large-scale AI infrastructure.

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.

Strong Lead Data Science, / AI Engineer / Machine Learning Engineer profiles.

2

Mandatory (Experience 1) - Must have 10+ years of experience in Data Science, AI/ML or AI Engineering with hands-on experience building production-grade ML systems.

3

Mandatory (Experience 2) - Must have hands-on experience building AI/ML solutions for Credit Risk, Fraud Risk Management (FRM), Collections & Recovery, with proven delivery of business-impacting AI/ML solutions.

4

Mandatory (Experience 3) - Candidate's Current designation must be Lead or above.

5

Mandatory (Experience 4) - Must have strong experience designing and deploying large-scale distributed Machine Learning systems, including model training, fine-tuning, inference, scalable serving, and production deployment.

6

Mandatory (Experience 5) - Strong programming experience in Python, along with exposure to Spark, Kafka, Kubernetes, APIs/Microservices, CI/CD, Feature Store, Model Registry, and Distributed Computing.

7

Mandatory (Experience 6) - Experience designing and deploying Credit Risk Models, Fraud Detection Models, Graph ML, Early Warning Systems, Portfolio Monitoring, Collections Optimization, Propensity Models, and Recovery Forecasting.

8

Mandatory (Experience 7) - Proven experience leading AI/ML teams, owning end-to-end delivery, mentoring engineers, driving cross-functional execution, and managing production AI platforms.

9

Mandatory (Experience 8) – Must have experience working under BFSI governance, including PII handling, auditability, model governance, compliance, secure-by-design architecture, approval workflows, and model risk management practices.

10

Mandatory ( Education ) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered

11

Mandatory (Age) - Candidate's Age should be below 37 years.

12

Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

13

Preferred (Experience 1) - Candidates currently working as Lead / Principal / Engineering Manager / Associate Director / Director in reputed Product, FinTech, Banking, NBFC, or Global Capability Centers will be preferred.

14

Preferred (Experience 2) - Indian professionals currently working overseas (NRI) who are planning to relocate and permanently settle in India are encouraged to apply.

15

Preferred (Experience 4) - Experience building enterprise AI platforms using Graph ML, Vector Databases, LLM-enabled decisioning, distributed training frameworks, and large-scale AI infrastructure.

Insurity’s Next Data Scientist:

We are seeking a Data Scientist to join our Predict team, focused on building and maintaining predictive models that support underwriting, claims, and audit use cases across Workers' Compensation and Commercial Auto. This role will be based in India and will play a key part in scaling our data science capabilities.

What Our Data Scientist Will Do: 

• Develop predictive models using GLM and machine learning techniques such as GBM, Random Forest, and XGBoost
• Perform feature engineering, selection, and transformation to optimize model performance
• Analyze structured and unstructured datasets to uncover insights and support model development
• Indentify and integrate third-party data sources to augment existing datasets and improve model accuracy
• Collaborate with product and engineering teams to integrate models into production environments
• Use AWS tools such as SageMaker and EC2 to build, train, and deploy models
• Document modeling decisions and communicate findings to technical and non-technical stakeholders
• Support model monitoring and performance tracking over time
•  

Who We’re Looking For:

• 2-5 years of experience in data science or predictive modeling
• Strong understanding of GLM and ML algorithms (GBM, XGBoost, Random Forest)
• Experience with Python and relevant libraries (scikit-learn, pandas, NumPy)
• Familiarity with AWS tools, especially SageMaker and EC2
• Experience with feature engineering and model evaluation techniques
• Ability to translate business problems into analytical solutions
• Strong communication skills and ability to work collaboratively in a cross-functional team
• Bachelor's or Master's degree in a quantitative field (Statistics, Mathematics, Computer Science, Data Science)
• Active listening
• Analytical and critical thinking
• Self-starter and quick learner
• Detail-oriented
• Ability to collaborate and work independently 
• Written and oral English communication
• Time management including work planning, prioritization, and organization 
• Sound judgement
• Ability to handle multiple priorities or tasks
• Flexible and adaptable

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.

Roles & Responsibilities

• Lead AI Product Pods across Credit Risk, Fraud, and Collections functions.
• Build and deploy production-scale Machine Learning systems for lending lifecycle decisioning.
• Own complete ML lifecycle including feature engineering, model training, evaluation, deployment, monitoring, and continuous improvement.
• Design scalable distributed ML infrastructure, feature stores, model registries, and MLOps pipelines.
• Develop AI solutions for underwriting, portfolio risk monitoring, fraud detection, anomaly detection, and recovery optimization.
• Drive model governance, monitoring, explainability, and compliance within BFSI regulatory standards.
• Collaborate with Product, Risk, Engineering, Data, and Business teams to deliver AI-driven business outcomes.
• Define AI platform architecture, operational excellence, SLAs, and incident management practices.
• Build, mentor, and scale high-performing AI Engineering and Data Science teams.

Ideal Candidate

1.Strong Lead Data Science, / AI Engineer / Machine Learning Engineer profiles.

2.Mandatory (Experience 1) - Must have 10+ years of experience in Data Science, AI/ML or AI Engineering with hands-on experience building production-grade ML systems.

3.Mandatory (Experience 2) - Must have hands-on experience building AI/ML solutions for Credit Risk, Fraud Risk Management (FRM), Collections & Recovery, with proven delivery of business-impacting AI/ML solutions.

4.Mandatory (Experience 3) - Candidate's Current designation must be Lead or above.

5.Mandatory (Experience 4) - Must have strong experience designing and deploying large-scale distributed Machine Learning systems, including model training, fine-tuning, inference, scalable serving, and production deployment.

6.Mandatory (Experience 5) - Strong programming experience in Python, along with exposure to Spark, Kafka, Kubernetes, APIs/Microservices, CI/CD, Feature Store, Model Registry, and Distributed Computing

7.Mandatory (Experience 6) - Experience designing and deploying Credit Risk Models, Fraud Detection Models, Graph ML, Early Warning Systems, Portfolio Monitoring, Collections Optimization, Propensity Models, and Recovery Forecasting.

8.Mandatory (Experience 7) - Proven experience leading AI/ML teams, owning end-to-end delivery, mentoring engineers, driving cross-functional execution, and managing production AI platforms.

9.Mandatory (Experience 8) – Must have experience working under BFSI governance, including PII handling, auditability, model governance, compliance, secure-by-design architecture, approval workflows, and model risk management practices.

10.Mandatory ( Education ) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered

11.Mandatory (Age) - Candidate's Age should be below 37 years.

12.Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

13.Preferred (Experience 1) - Candidates currently working as Lead / Principal / Engineering Manager / Associate Director / Director in reputed Product, FinTech, Banking, NBFC, or Global Capability Centers will be preferred.

14.Preferred (Experience 2) - Indian professionals currently working overseas (NRI) who are planning to relocate and permanently settle in India are encouraged to apply.

15.Preferred (Experience 4) - Experience building enterprise AI platforms using Graph ML, Vector Databases, LLM-enabled decisioning, distributed training frameworks, and large-scale AI infrastructure.

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

Job Description – Data Scientist (Machine Learning & Forecasting)

About the Role

We are looking for a highly skilled Data Scientist with strong expertise in Machine Learning, Traditional Statistical Modelling, Forecasting, and Predictive Analytics. The ideal candidate will have hands-on experience building and deploying end-to-end ML solutions, working with large datasets, and translating business problems into scalable data science solutions.

The role requires a strong foundation in statistics, predictive modelling, feature engineering, model evaluation, and time-series forecasting, along with the ability to collaborate with cross-functional teams to deliver business impact.

Key Responsibilities

• Design, develop, and deploy Machine Learning models for business-critical use cases.
• Build and optimize traditional ML models such as:
• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• Gradient Boosting (XGBoost, LightGBM, CatBoost)
• Support Vector Machines
• Clustering Algorithms
• Develop forecasting solutions using:
• ARIMA / SARIMA
• Prophet
• Exponential Smoothing
• Time-Series Regression Models
• Perform exploratory data analysis (EDA), feature engineering, and data validation.
• Evaluate model performance using appropriate statistical and business metrics.
• Work with structured and semi-structured datasets from multiple sources.
• Collaborate with business stakeholders to understand requirements and translate them into analytical solutions.
• Build scalable data pipelines and support model deployment in production environments.
• Monitor model performance, identify data drift, and implement model retraining strategies.
• Present insights and recommendations to technical and non-technical stakeholders.

Required Skills & Qualifications

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or a related quantitative field.
• 5+ years of hands-on experience in Data Science, Machine Learning, and Forecasting.

Technical Skills

   Machine Learning

• Strong understanding of supervised and unsupervised learning algorithms.
• Experience with ensemble methods and advanced ML techniques.
• Expertise in model selection, hyperparameter tuning, and performance optimization.

Forecasting & Statistics

• Strong understanding of:
• Time-Series Analysis
• Forecasting Techniques
• Statistical Inference
• Hypothesis Testing
• Probability Distributions
• A/B Testing

Programming

• Advanced proficiency in Python.
• Experience with:
• Pandas
• NumPy
• Scikit-learn
• Statsmodels
• XGBoost / LightGBM
• Prophet

Data & SQL

• Strong SQL skills with experience in complex queries and performance optimization.
• Experience working with large-scale datasets.

Visualization

• Experience with Power BI, Tableau, Matplotlib, Seaborn, or Plotly.
• Cloud & MLOps (Preferred)
• Exposure to AWS, Azure, or GCP.
• Understanding of Docker, Kubernetes, CI/CD, and ML model deployment practices.

Key Competencies

• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder management abilities.
• Ability to work independently in a fast-paced environment.
• Strong business acumen and data-driven decision-making mindset. 

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)

🚀 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

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