Mumbai Maharashtra Senior network security engineer Jobs – 144 Open Jobs Today

Data Scientist
Programming.com —Mumbai, Maharashtra
- ₹6,00,242.14 - ₹19,76,587.39 a year
- Full-time
- Computer Science
- Master's degree
- SQL
- Health insurance
- Paid sick time
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10d
Partner Solutions Engineer - Mumbai
Cisco Systems —Mumbai, Maharashtra
- Full-time
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NEC Software Solutions (India) —Mumbai, Maharashtra
- Full-time
- Analysis skills
- C++
- APIs
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Architect role
Route Mobile —Mumbai, Maharashtra
- Full-time
- English
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Software Engineer Developer
AryaXAI —Unknown, Maharashtra
- Full-time
- Hybrid work
- Databases
- AWS
- C++
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COO Analyst
Barclays —Mumbai, Maharashtra
- Project management
- Bachelor's degree
- Communication skills
3h

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Data Scientist

Programming.com -
Mumbai, Maharashtra

Quick apply

Job details

Permanent | Full-time
₹6,00,242.14 - ₹19,76,587.39 a year
10 days ago

Benefits

Provident Fund
Health insurance
Paid sick time

Qualifications

BCS
TensorFlow
Computer Science
PyTorch
NumPy
Git
Master's degree
SQL
Pandas
Bachelor's degree
APIs
Fraud prevention and detection
Data science
Clustering
AI
Banking
Python
Data Science

Full job description

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

Quick apply

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