Data Science Team (Senior & Junior Roles) – Mulehunter.ai
Job Location
- Mumbai, Maharashtra (On-site / Hybrid)
About Mulehunter.ai & Why These Roles Matter
Mulehunter.ai is an advanced AI/ML-based platform designed to detect mule accounts across the banking ecosystem, with a specialized focus on scaling fraud security within cooperative and rural banking networks. As new banks are onboarded, our platform must be continuously tuned to adapt to a new class of customers, products, and evolving transaction patterns.
To support our rapid expansion, we are scaling our Mumbai-based Data Science team. We are looking for a Senior Data Scientist to provide technical leadership and architectural direction, alongside a Junior Data Scientist to drive hands-on execution, analysis, and experimentation.
Role 1: Senior Data ScientistWhat You Will Do
- Technical Leadership: Lead end-to-end model design covering data exploration, feature engineering, training, calibration, validation, and production deployment.
- Advanced Modeling: Train, evaluate, and scale supervised, unsupervised, and anomaly-detection models for identifying complex mule-account networks.
- Feature Engineering: Partner with AML/FRM teams to translate banking insights and red-flag behaviors into high-performing, model-ready features.
- Team & Standards: Define ML standards, code-review practices, experiment tracking, and documentation norms. Mentor junior data scientists and review their technical execution.
- Integration: Collaborate closely with data engineers and bank IT teams for seamless deployment via APIs.
What We Are Looking For
- Education: Bachelor’s degree in Computer Science, Data Science, AI/ML, or a related field (Master’s preferred).
- Experience: 6–8 years of hands-on experience in Data Science and MLOps, ideally within banking, financial crime, or AML domains.
- Tech Stack: Strong programming in Python and SQL. Deep familiarity with ML frameworks (TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM) and large-scale transactional datasets.
- Infrastructure: Comfort with on-prem ML environments (Anaconda/Jupyter, VS Code, Kubeflow, Git, PySpark).
- Domain Knowledge: Strong understanding of AML concepts including RFIs, STR workflow, and alert-review processes.
Role 2: Junior Data ScientistWhat You Will Do
- Data Prep & EDA: Perform data quality checks, pre-processing, and exploratory data analysis to surface behavioral indicators and fraud red flags.
- Model Support: Build, train, and evaluate baseline supervised and unsupervised models under the guidance of senior data scientists.
- Production Monitoring: Monitor live model performance, analyze data drifts, investigate false positives/negatives, and propose tuning actions.
- Feedback Loops: Work closely with the Enhanced Due Diligence (EDD) team to refine thresholds and continuously improve alert precision.
- Documentation: Keep rigorous records of detection rules, model versions, and experiment results.
What We Are Looking For
- Education: Bachelor’s degree in Computer Science, Data Science, AI/ML, Statistics, or a related field.
- Experience: 2–5 years of applied ML experience, preferably with exposure to banking, fraud, or transactional datasets.
- Tech Stack: Proficiency in Python and SQL, with hands-on experience using pandas, NumPy, scikit-learn, XGBoost, and LightGBM. (PySpark is a major plus).
- Core Concepts: Clear understanding of core classification, clustering, and anomaly-detection techniques.
- Workflow: Comfort using Anaconda/Jupyter, VS Code, and Git-based version control, alongside an eagerness to learn financial compliance systems.
What Success Looks Like for Our Team
- Production Excellence: Deploying highly accurate, production-grade fraud detection models across our participating banks.
- Operational Efficiency: Materially reducing false-positive rates while significantly improving model interpretability for bank compliance officers.
- Seamless Integration: Achieving successful validation and seamless integration with existing bank AML/FRM infrastructures.
Pay: ₹600,242.14 - ₹1,976,587.39 per year
Benefits:
- Health insurance
- Paid sick time
- Provident Fund
Work Location: In person
