Project Delivery And Implementation Manager

1. Position Summary

Senior Project Manager

2. Key Responsibilities

A. Project and Program Management

• Lead the planning, execution, monitoring, and closing of the complex fraud detection project, ensuring adherence to scope, budget, and timeline.
• Develop comprehensive project plans, including detailed work breakdown structures (WBS), resource allocation, and risk management strategies.
• Manage project documentation, track progress against milestones, and conduct regular project review meetings.
• Implement and champion Agile/Scrum methodologies tailored for a Data Science/ML development lifecycle.

B. Stakeholder and Communication Management

• Serve as the primary point of contact and liaison between the project team, client, technical specialists (Data Scientists, Engineers), and senior management.
• Proactively manage and communicate expectations, project status, risks, and issues to all internal and external stakeholders through clear and concise reporting.

C. Technical Oversight and Data Science Delivery

• Provide leadership and guidance to the Data Science team, ensuring the effective development and deployment of

  Machine Learning algorithms

  for healthcare insurance claims fraud detection (e.g., anomaly detection, predictive modeling).
• Must have

  prior hands-on experience managing data science projects with ML inclusion

  and understand the nuances of the ML lifecycle (e.g., data ingestion, feature engineering, model training, validation, deployment, and monitoring).
• Oversee the

  Data Analytics

  pipeline, ensuring data quality, integration, and preparation are optimized for ML model consumption.
• Drive the design, development, and maintenance of interactive

  Tableau Dashboards

  to visualize fraud patterns, model performance metrics (Precision, Recall, AUC), and operational insights for both technical and non-technical stakeholders.

D. Infrastructure and System Knowledge

• Possess foundational knowledge of

  infrastructure and server environments

  (cloud or on-premise) necessary to host and scale the ML models and data pipelines.

3. Required Qualifications

A. Education and Experience

• Minimum of

  5+ years

  of progressive experience in Project or Program Management.
• Proven track record of successfully leading at least

  one full-cycle Data Science project

  where Machine Learning models were a central component of the solution (e.g., fraud, risk, or recommendation systems).
• Educational background must include a B.E. / B.Tech. or MBA from a reputed institution. PMP/PRINCE2 certification is a strong plus.

B. Technical and Domain Skills

• Deep understanding of

  Machine Learning algorithms

  and their application in

  fraud detection

  or similar classification problems.
• Expert proficiency in

  Data Analytics

  and the ability to interpret complex data insights.
• Demonstrated experience in developing and presenting insights using

  Tableau Dashboards

  (or similar advanced BI tools).
• Working knowledge of data infrastructure, SQL, and concepts of server deployment/maintenance.