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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.

At Dolat Capital, we blend cutting-edge technology with quantitative finance to drive high-performance trading across Equities, Futures, and Options. We're a fast-moving team of traders, engineers, and data scientists building ultra-low latency systems and intelligent trading strategies.

🎯 What You’ll Work On

1. Designing and deploying high-frequency, high-sharpe trading strategies

2. Building low-latency, high-throughput trading infrastructure (C++/Python/Linux).

3. Leveraging AI/ML to detect alpha and market patterns from large datasets

Real-time risk systems, simulation tools, and performance optimization

4. Collaborating across tech and trading teams to push innovation in live markets.

🧠 What We’re Looking For

1. Master’s (U.S.) in CS or Computational Finance (MANDATORY)

2. 1–2 years of experience in a quant/tech-heavy role

3. Strong in C++, Python, algorithms, Linux, TCP/UDP

4. Experience with AI/ML tools like TensorFlow, PyTorch, or Scikit-learn

5. Passion for high-performance systems and market innovation.

About UpSolve

We built and deliver complex AI solutions which help drive business decisions faster and more accurately. We are a typical AI company and have a range of solutions developed on Video, Image and Text.

What you will do

• Stay informed on new technologies and implement cautiously
• Maintain necessary documentation for the project
• Fix the issues reported by application users
• Plan, build, and design solutions with a mental note of future requirements
• Coordinate with the development team to manage fixes, code changes, and merging

Location: Mumbai

Working Mode: Remote

What are we looking for

• Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.
• Minimum 2 years of professional experience in software development, with a focus on machine learning and full stack development.
• Strong proficiency in Python programming language and its machine learning libraries such as TensorFlow, PyTorch, or scikit-learn.
• Experience in developing and deploying machine learning models in production environments.
• Proficiency in web development technologies including HTML, CSS, JavaScript, and front-end frameworks such as React, Angular, or Vue.js.
• Experience in designing and developing RESTful APIs and backend services using frameworks like Flask or Django.
• Knowledge of databases and SQL for data storage and retrieval.
• Familiarity with version control systems such as Git.
• Strong problem-solving and analytical skills.
• Excellent communication and collaboration abilities.
• Ability to work effectively in a fast-paced and dynamic team environment.
• Good to have Cloud Exposure