Credit & Risk Strategist (B2C, Fintech)

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.

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.

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.

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.

Strong Senior Product Manager — Vertical Products (Fintech, Growth & Conversion) Profile

2

Mandatory(Experience 1)- Must have 6+ years of product management experience, with the most recent 3 years spent owning a consumer-facing product surface (app, web funnel, onboarding, checkout, or equivalent B2C surface).

3

Mandatory(Experience 2) - Must have consumer-facing product ownership as the centre of gravity. Backend, API, infra, or internal-tools PMs with no recent consumer-facing surface will not be considered. Backend exposure alongside real consumer-funnel work is fine; a backend centre not.

4

Mandatory(Experience 3)- Must have a metric-backed track record improving conversion, revenue yield, engagement, or lead quality in a consumer funnel — owned numbers, a steady experimentation cadence (A/B tests, rollouts, readouts), and comfort with analytics: funnels, cohorts, drivers, causal reasoning, measurement hygiene.

5

Mandatory(Experience 4)- Must be fluent with AI — active, habitual use of AI tools (ChatGPT, Claude, Gemini, Perplexity, or equivalent) in daily product practice.

6

Mandatory(Stakeholder Mgmt)- Must be able to act as the primary link between business leaders, tech teams, and external partners — communicating roadmap, priorities, trade-offs, and performance readouts to senior stakeholders, and working with distributed teams across time zones (Australia, India, Philippines).

7

Mandatory(Product Writing & Agile Delivery)- Must have strong product writing — problem briefs, PRD-lite/user stories, experiment plans, decision logs — and deep working command of Agile frameworks and tools.

8

Mandatory(Company) - Must be from B2C product company. FinTech is the priority . A non-fintech candidate qualifies only if currently or recently at a Series B plus company.

9

Preferred(Skill)- Exposure to LangChain, RAG, or prompt engineering in a product context; experience working directly with data science/ML teams on model-backed features; prior AI features in transactional or financial-services consumer UX.

Designation - Product Manager

Experience - 4 to 6 Years

Work Location - Whitefield, Bengaluru

We’re hiring Product Managers to own product lines within our LOS platform end-to-end. You’ll work directly with credit union clients to gather requirements, write user stories, run sprints with your engineering pod, manage vendor integrations, and ship features used by real customers. You’ll also lead full client implementations — from discovery to go-live.

Beyond execution, you’ll help shape the platform roadmap by identifying patterns across clients and driving what should be configurable vs. custom to scale the product. This is a high-ownership, hands-on role — focused on real problem-solving, close collaboration with users and engineers, and making practical trade-offs to deliver impact.

A typical week looks like

• 2-3 client calls with US credit union stakeholders — CXOs, managers, underwriters,
• Act as the bridge between US team & India Engineering team
• Writing PRFAQs, stories and acceptance criteria for upcoming work
• Sprint ceremonies with your engineering pod — planning, grooming, retro
• Coordinating with 2-3 vendor integration teams — API specs, test data, certification
• Ensuring alignment across organisation for delivery for your product line & client implementation

What you'll ship in the first 6 months

• Own a product line end-to-end
• Own a client implementation from requirements through go-live
• Ship features that go live in production for real credit union users
• Manage 3-5 vendor integrations through to QA sign-off
• Run your pod's sprint cycle independently
• Contribute at least one platform-level improvement that benefits all clients

What we're looking for

• Biased for action, grounded in first principles — you ship fast, but you think before you build.
• AI-native — you use AI tools daily to multiply your output, not as a novelty. You write specs faster, research deeper, and make better decisions because AI is part of how you work.
• Comfortable working directly with clients (US business hours overlap required — expect 2-3 late evening calls per week)
• Writes clearly — PRFAQs, story documents, acceptance criteria
• Can own an engineering pod's backlog — sprint planning, grooming, prioritization
• Understands lending workflows — origination, decisioning, underwriting, funding
• Can navigate vendor integrations — read API docs, coordinate with external teams, unblock engineers
• Uses data and judgment to make scope calls fast — you won't have perfect information
• Takes feedback well and gives it directly

Qualifications

Required:

• 3-5 years total experience with at least 2 years in product management
• Lending, banking, or fintech domain experience
• Experience working with engineering teams in an agile environment
• Actively uses AI tools in daily PM work — research, writing, analysis, decision-making
• Familiar with Claude Code to leverage AI & work at 10x productivity
• BTech/BE. MBA preferred.

Preferred / Bonus:

• Familiar with Claude Code to leverage AI & work at 10x productivity
• US lending industry experience
• US indirect auto lending (dealer-lender workflows, RouteOne, DealerTrack)
• US home equity / HELOC product experience
• Credit union operations experience
• Experience with loan origination systems (any vendor)
• Familiarity with core banking integrations (Jack Henry, Fiserv, Corelation)
• Familiarity with credit bureau data (TransUnion, Equifax, Experian)
• Experience with decision engines or business rules platforms

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.

Strong Senior Product Manager — Vertical Products (Fintech, Growth & Conversion) Profile

2

Mandatory(Experience 1)- Must have 6+ years of product management experience, with the most recent 3 years spent owning a consumer-facing product surface (app, web funnel, onboarding, checkout, or equivalent B2C surface).

3

Mandatory(Experience 2) - Must have consumer-facing product ownership as the centre of gravity. Backend, API, infra, or internal-tools PMs with no recent consumer-facing surface will not be considered. Backend exposure alongside real consumer-funnel work is fine; a backend centre not.

4

Mandatory(Experience 3)- Must have a metric-backed track record improving conversion, revenue yield, engagement, or lead quality in a consumer funnel — owned numbers, a steady experimentation cadence (A/B tests, rollouts, readouts), and comfort with analytics: funnels, cohorts, drivers, causal reasoning, measurement hygiene.

5

Mandatory(Experience 4)- Must be fluent with AI — active, habitual use of AI tools (ChatGPT, Claude, Gemini, Perplexity, or equivalent) in daily product practice.

6

Mandatory(Stakeholder Mgmt)- Must be able to act as the primary link between business leaders, tech teams, and external partners — communicating roadmap, priorities, trade-offs, and performance readouts to senior stakeholders, and working with distributed teams across time zones (Australia, India, Philippines).

7

Mandatory(Product Writing & Agile Delivery)- Must have strong product writing — problem briefs, PRD-lite/user stories, experiment plans, decision logs — and deep working command of Agile frameworks and tools.

8

Mandatory(Company) - Must be from B2C product company. FinTech is the priority . A non-fintech candidate qualifies only if currently or recently at a Series B plus company.

9

Preferred(Skill)- Exposure to LangChain, RAG, or prompt engineering in a product context; experience working directly with data science/ML teams on model-backed features; prior AI features in transactional or financial-services consumer UX.

We’re looking for a dynamic and driven Data Analyst to join our team of technology enthusiasts. This role is crucial in transforming data into insights that support strategic decision-making and innovation within the insurance technology (InsurTech) space. If you’re passionate about working with data, understanding systems, and delivering value through analytics, we’d love to hear from you.

What We’re Looking For

• Proven experience working as a Data Analyst or in a similar analytical role
• 3+ Years of experience in the field
• Strong command of SQL for querying and manipulating relational databases
• Experience with Power BI for building impactful dashboards and reports
• Familiarity with QlikView and Qlik Sense is a plus
• Ability to communicate findings clearly to technical and non-technical stakeholders
• Knowledge of Python or R for data manipulation is nice to have
• Bachelor’s degree in Computer Science, Statistics, Mathematics, Economics, or a related field
• Understanding of the insurance industry or InsurTech is a strong advantage

What You’ll Be Doing:

• Delivering timely and insightful reports to support strategic decision-making
• Working extensively with Policy Administration System (PAS) data to uncover patterns and trends
• Ensuring data accuracy and consistency across reports and systems
• Collaborating with clients, underwriters, and brokers to translate business needs into data solutions
• Organizing and structuring datasets, contributing to data engineering workflows and pipelines
• Producing analytics to support business development and market strategy

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