Sr. Applied Data Scientist / ML Lead

Role overview

Sr. Applied Data Scientist / ML Lead at FCI CCM

The role will involve building, challenging and improving machine learning models while creating a lasting analytical capability within FCI beyond the initial engagement.

Responsibilities

• Build and evaluate approaches for: Transaction classification Behavioural segmentation Recommendation and ranking Customer propensity and targeting Select the right approach for each problem, whether simple statistical baselines or specialized machine learning models. 2.
• Ensure the solution does not rely on continuous public internet access or managed ML platforms.
• Build deployment approaches that support: Containerized inference Offline dependency management Secure model execution Self-hosted monitoring and feature management 7.
• Support secure handling of sensitive banking and transaction data through: PII masking Encryption at rest and in transit Secrets management Access controls Secure self-hosted tooling Support specialist-led assessment of privacy-preserving techniques such as differential privacy and federated learning, wherever required.
• Deliver at least one independently implemented analytical improvement.
• Support reuse and deployment of the solution for a second banking client.

Required qualifications

• Typically 6–10 years of experience in Applied Data Science / Machine Learning.
• Strong hands-on experience in Python and SQL.
• Practical experience with scikit-learn and modelling frameworks such as XGBoost, LightGBM, CatBoost or PyTorch.
• Strong evidence of personally delivering and maintaining production ML models.
• Experience handling model failures, monitoring, retraining and subsequent improvements.
• Strong understanding of: Statistics, Feature engineering, Experimental design, Selection bias, Confounding, Treatment/control methodologies, Uncertainty and result interpretation Experience working with large structured event or transaction datasets.
• Ability to work with noisy labels, missing data and changing customer behaviour.
• Experience collaborating with Data Engineering teams on scalable computation.
• Strong ability to review and test code beyond exploratory notebooks.
• Strong documentation, mentoring and stakeholder communication skills.
• Ability to challenge experts constructively and explain analytical decisions to non-technical stakeholders Benefits The company offers a range of employee benefits including: Cashless medical insurance for employees, spouses, and children Accidental insurance coverage Life insurance coverage Retirement benefits including Provident Fund (PF) and Gratuity ESI* Complementary meal coupons Company-paid transportation Sodexo benefits for income tax savings Paternity & Maternity Leave Benefit National Pension Saving EL encashment Sick Leave

Employment details

• Location: Noida [Uttar Pradesh] [India]

Additional role details

• Join the Legacy of Innovation with FCI-CCM: Backed by a legacy of innovation since 1959, FCI has built over six decades of expertise in communications, continuously evolving to meet changing business and technology needs.
• By combining deep domain knowledge with innovation, FCI has consistently delivered enterprise-grade communication solutions that help organizations adapt to an increasingly digital world.
• Today, FCI continues that legacy through its flagship platform, VARTA, an Insights-Led Customer Communications Platform that empowers organizations to unify customer intelligence, communication workflows, behavioural signals, and engagement orchestration to deliver intelligent, personalized, and real-time customer experiences.
• Broad Function: FCI is building advanced AI capabilities to help banks convert transactions and behavioral signals into relevant customer engagement and measurable growth.
• The initial focus will be on credit cards, with a product designed for reuse across multiple banks and deployment within bank-controlled environments.
• The Senior Applied Data Scientist / ML Lead will act as FCI’s hands-on analytical lead and will work closely with our banking/data-science co-development partner, along with internal Product, Architecture and Engineering teams.
• Roles and Responsibilities (not limited to): 1.
• Applied Data Science & Model Development: Translate banking growth hypotheses into meaningful features, labels, models and measurable experiments in collaboration with the Product team and banking SMEs.
• Model Evaluation & Experimentation: Own the complete model evaluation framework, including: Time-based holdouts Leakage prevention Class imbalance handling Model calibration Segment-level error analysis Treatment/control evaluation Separation of predictive accuracy from incremental business impact Ensure that model success is measured not only through technical accuracy but also through measurable business outcomes. 3.
• Production ML & Engineering Write production-quality Python, tests and reproducible training/evaluation pipelines.
• Work closely with Data Engineering and MLOps teams on: Model deployment Monitoring Retraining Rollback Automated validation CI/CD for ML workflows Ensure models are maintainable, reproducible and capable of being operated by other FCI engineers and client teams. 4.
• Partner Co-Development & Knowledge Transfer Work hands-on with FCI’s banking/data-science co-development partner.
• Reproduce partner results independently, challenge assumptions constructively and document: Model assumptions Feature definitions Evaluation logic Rejected alternatives Model limitations and risks Mentor FCI colleagues through practical implementation to build internal capability and reduce dependency on external partners. 5.
• Reusable Multi-Bank Architecture: Maintain a clean separation between: Reusable Core Model code Feature interfaces Evaluation logic Training workflowsn and Bank-Specific Components Data mappings Thresholds Configurations Client-specific artefacts Support configurable onboarding of the product for subsequent banks without rebuilding the entire solution each time. 6.
• On-Premises / Private Cloud Deployment: Design ML solutions for bank-controlled environments, including on-premises and private-cloud deployments.
• Model Governance, Security & Privacy: Work within bank security controls and maintain proper model provenance, documentation and auditability.
• Success in First 6-9 Months: First 90 Days: Reproduce partner baseline models.
• Establish evaluation methodology and feature documentation.
• By Month 6: Operate and improve agreed model workflows.
• Demonstrate reproducibility and segment-level performance.
• Clearly explain model logic and business rationale to Product and Engineering stakeholders.
• By Month 9: Lead a controlled model or use-case enhancement.
• Enable at least one additional FCI colleague to independently execute critical ML workflows.
• Requirements Desired Qualifications &