Lead Data Scientist - Artificial Intelligence/Machine Learning

Description :

Key Responsibilities :

- Collaborate with the AI Product Owner to understand the business requirements and define appropriate modelling approaches, experimentation plans, and success metrics.

- Coordinate with business teams to monitor model outcomes, gather feedback, and refine/improve machine learning models based on performance insights.

- Lead data discovery, feature engineering, experimentation, offline/online evaluation, and productionization with CI/CD for ML; own model documentation, reproducibility, and traceability.

- Apply supervised/unsupervised/deep learning, NLP, and LLM techniques (including RAG pipelines, prompt engineering, vector search, and safety guardrails) where they create clear value.

- Design and execute rigorous evaluation strategies for ML and GenAI models, including offline metrics, human in the loop reviews for GenAI outputs, regression checks, and failure mode analysis.

- Implement governance frameworks for AI models applying bias/fairness checks, safety filters, responsible AI controls, and executing evaluation protocols defined by Business.

- Collaborate with data/ML engineers to industrialize models via APIs/batch jobs, feature stores, scalable serving, and monitoring for drift, performance, cost, and latency.

- Lead data mining, collection, and quality initiatives across structured, semistructured, and unstructured data to ensure integrity, lineage, and compliance.

- Maintain rigorous experiment tracking using tools, ensuring reproducibility and clear lineage across model iterations and experiments.

- Adhere to stringent quality assurance and documentation standards using version control and code repositories (e.g., Git, GitHub, Markdown)

- Mentor and lead data scientists, conduct design/code reviews, and cultivate best practices in experimentation, evaluation, and documentation.

- Track emerging tools/techniques in ML/GenAI and drive reusable frameworks, templates, and SDK/APIbased accelerators to industrialize solutions across the organization.

Required Skills & Qualifications :

Technical Experience :

- 5- 8 years of hands-on experience across classical ML (treebased methods, GLMs), deep learning (PyTorch/TensorFlow), and NLP/LLMs (tokenization, embeddings, finetuning, instructiontuning, RAG).

- Hands on with evaluation and safety/guardrail patterns for production GenAI.

- Familiarity with ML lifecycle platforms (such as SageMaker, Azure ML, or Databricks) to run experiments, track models, and provide wellstructured model artifacts to ML Engineers for deployment

- Comfortable with AWS services for data/ML (e.g., S3, Glue, EMR/Spark, Lambda, SageMaker; Databricks), and integrating with enterprise data lakes/warehouses.

- Proficient in Python and ML/DS libraries (Pandas, scikitlearn, PyTorch/TensorFlow, XGBoost/LightGBM); strong software practices (testing, linting, packaging).

- Strong SQL and data wrangling; experience with Spark/Databricks for largescale feature pipelines and training.

- Working knowledge of data privacy, safe model behaviors, prompt filtering/output moderation, and auditability for regulated environments.

- Exploratory data analysis and hypothesis testing to identify ML opportunities is a plus.

- Experience with dashboards/BI (Power BI/Tableau) and experiment tracking (e.g., MLflow) is a plus.

Consulting Experience :

- Proven track record in an IT consulting environment, engaging with large enterprises and MNCs in strategic

data solutioning projects.

- Strong stakeholder management, business needs assessment, and change management skills.

Leadership & Soft Skills :

- Experience managing and mentoring small teams, developing technical skills AI & Advanced Analytics domains.

- Ability to influence and align cross-functional teams and stakeholders.

- Excellent communication, documentation, and presentation skills.

- Strong problem-solving, analytical thinking, and strategic vision.

Educational Qualifications :

- Bachelors or Masters degree in Computer Science, Engineering, Data Science, or a related quantitative field.

Preferred Certifications :

- AWS Certified Machine Learning Specialty

- AWS Certified Data Analytics Specialty (or equivalent)

- Databricks Machine Learning Professional and/or Databricks Generative AI Engineer (plus)

- Certified Artificial Intelligence Practitioner (CAIP) or similar GenAI/Responsible AI certifications

What Were Looking For :

- Self-starters who are highly motivated, ambitious, and eager to challenge the status quo.

- Builders who combine scientific rigor with pragmatic engineering and can balance accuracy, latency, and cost.

- Effective leaders who collaborate openly, freely share knowledge and elevate team performance.

- Straightforward, results-oriented individuals who value impact and accountability.

- Adaptable experts who stay on top of fast-evolving AI technologies and practices.