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Animesh Banerjee

@animeshbanerjee

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AI governance and control remediation specialist combining policy rigor with computational data integrity.

India

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What I'm looking for

I seek a role applying AI governance and control remediation skills to operationalize audit‑ready, policy‑aligned data workflows in high‑risk sectors, with opportunities for measurable impact and cross‑functional collaboration.

I am an AI governance and control remediation specialist with a gold‑medalist M.Sc. in Computational Biology from JNU, applying UPSC‑tier policy analysis and computational rigor to high‑risk regulatory challenges.

I design and implement scalable governance solutions that embed frameworks such as NIST AI RMF, EU AI Act, and ISO 42001 into operational pipelines, ensuring auditability and continuous compliance.

My technical approach leverages Python (Pandas/NumPy/StatsModels), R, MICE, PCA, SQL (inferred) and system dynamics simulation to operationalize trusted data workflows and mitigate bias in high‑stakes domains like FinTech.

I have delivered functional prototypes and research outcomes that improved review efficiency and reduced escalation latency, and I seek roles where I can translate policy insight and quantitative modeling into measurable control remediation impact.

Experience

Work history, roles, and key accomplishments

Current

Control Remediation Specialist

Current

Rebounce

Jul 2025 - Present (5 months)

Led data integrity and policy forecasting work, engineering a Quality-of-Growth index using microdata and PCA to remove subjective bias and produce regulatory recommendations; applied elasticity simulations to inform policy decisions.

Data Integrity Statistical Modeling Policy Analysis Regulatory

AI Governance Specialist

Credo Alignment Prototype

Oct 2025 - Nov 2025 (1 month)

Implemented governance-ready controls in an AI workspace embedding NIST AI RMF and ISO principles, defining HITL checkpoints, RBAC and audit-trail automation, and demonstrated a 35% increase in review efficiency and 40% reduction in escalation latency.

AI Governance NIST AI RMF ISO 42001 RBAC Policy as Code HITL Telemetry

Computational Modeling Researcher

Jawaharlal Nehru University

Sep 2022 - Aug 2023 (11 months)

Executed high-throughput computational modeling for M.Sc. dissertation analyzing system stability with 200k+ variables to assess risk and failure modes transferable to financial risk modeling.

Python R Biostatistics Risk Assessment Simulation

Education

Degrees, certifications, and relevant coursework

Jawaharlal Nehru University

Master of Science, Computational Biology

2021 - 2023

M.Sc. in Computational Biology with focus on biostatistics, system dynamics, and machine learning; DBT fellow and thesis on high-throughput modeling for risk and stability assessment.