Lead the development of AI-powered solutions for legal and intellectual asset management, leveraging machine learning, generative AI, and big data technologies.

About the Company

Iota Analytics is a legal data automation firm headquartered in the UK with offices in the US and India (Chandigarh, Mohali, Noida, and now Bangalore). With a vision to be a leading product-led provider of legal and intellectual asset management (IAM) services, the company serves corporate counsels, law firms, and R&D leads across the US and Europe. The company is at the cutting edge of legal and IPR services, blending technology, data, and a talented team of over 250 professionals including attorneys, engineers, and data scientists. Iota Analytics operates three business lines: Smart Intellectual Asset Management Engine (industry-specific insights leveraging curated patent data), Legal-tech and Process Automation (smart e-Discovery, Automation, and Research), and IAM Consulting (IP consulting spanning ideation to commercialisation). The management team is highly experienced and diverse, emphasising values of quality, shared value creation, and data privacy.

Role Overview

We are seeking a highly skilled Principal Data Scientist to develop AI-powered solutions using AI/ML and Generative AI, transforming complex datasets into actionable business strategies. This mid-to-senior-level position requires leadership in data science initiatives, statistical analysis implementation, and cross-functional collaboration to address organizational challenges. The ideal candidate will demonstrate proficiency in machine learning algorithms, big data frameworks, cloud computing platforms (AWS/Azure/GCP), AI and Gen AI technologies (NLP, computer vision, reinforcement learning), and programming languages (Python, R, SQL). Core responsibilities include implementing end-to-end Gen AI solutions, machine learning pipelines, optimizing data infrastructure, mentoring junior analysts, and effectively communicating technical concepts to non-technical stakeholders.

Key Responsibilities

• Design and implement domain-specific Gen AI and machine learning systems, architecting production-grade models using PyTorch, TensorFlow, and scikit-learn to automate contract analysis, case outcome prediction, and legal document classification.
• Develop advanced data structures including specialized schemas, knowledge graphs, and ontologies to represent complex legal relationships and precedent networks.
• Orchestrate secure and compliant ML pipelines on cloud platforms (AWS SageMaker, Azure ML, GCP Vertex AI) with attention to data sovereignty and client confidentiality.
• Drive NLP applications by fine-tuning transformer architectures (BERT, GPT, T5) for legal language processing tasks such as precedent analysis, citation linking, and regulatory compliance monitoring.
• Implement data modeling: create dimensional models, feature stores, and data warehousing solutions optimized for legal analytics with versioning and lineage tracking.
• Lead data architecture design by engineering distributed computing solutions (Spark, Dask, Ray) to process large-scale legal document repositories and regulatory frameworks.
• Develop ETL pipelines to normalize, cleanse, and transform heterogeneous legal data sources (court records, contracts, regulations) into unified analytical datasets.
• Apply statistical analysis for risk assessment using Bayesian methods, causal inference, and experimental design to quantify legal risks and predict litigation outcomes.
• Architect secure document processing systems using OCR, NER, and knowledge graphs to extract and structure legal entities and relationships.
• Develop explainable AI decision support using SHAP, LIME, and feature importance techniques for transparent model behavior meeting judicial scrutiny.
• Establish AI governance frameworks with data validation, model versioning, and audit trails to ensure regulatory and ethical compliance.

Tech Stack

• Languages: Python (NumPy, Pandas, SciPy, Scikit-learn), R, SQL
• ML/DL Frameworks: TensorFlow, PyTorch, scikit-learn
• Big Data & Distributed Computing: Spark, Dask, Ray
• Cloud Platforms: AWS (SageMaker), Azure (ML), GCP (Vertex AI)
• Gen AI & NLP: BERT, GPT, T5, RAG, fine-tuning, LLM orchestration
• Model Interpretability: SHAP, LIME
• Data Processing: OCR, NER, Knowledge Graphs
• Data Warehousing & ETL: Feature stores, dimensional modeling
• MLOps: CI/CD for model training/deployment, model versioning, audit trails

Masters/Bachelors in Computer Science with specialization in Data Science, Machine Learning, Statistics, or related quantitative field. 10+ years hands-on data science experience, 3+ years in technical leadership.

Continuous learning environment, cross-business opportunities, and international movement possibilities.