Job Title

Lead Data ScientistRole OverviewAt Schneider Electric’s Digital Technology Centres (DTCs), we are building a next generation enterprise AI Delivery team and are seeking an experienced, Lead Data Scientist with deep technical expertise and a strong business acumen. You will focus on driving modern AI innovation, leading advanced ML and GenAI research, experimentation, model design, and prototyping (including LLMs, RAG, and cutting‑edge deep learning methods) – collaborating with product, business, and data engineering partners to shape high‑impact AI solutions.Key ResponsibilitiesCollaborate 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, semi‑structured, 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/API‑based accelerators to industrialize solutions across the organization.Required Skills & QualificationsTechnical Experience:5–8 years of hands-on experience across classical ML (tree‑based methods, GLMs), deep learning (PyTorch/TensorFlow), and NLP/LLMs (tokenization, embeddings, fine‑tuning, instruction‑tuning, 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 well‑structured model artifacts to ML Engineers for deploymentComfortable 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, scikit‑learn, PyTorch/TensorFlow, XGBoost/LightGBM); strong software practices (testing, linting, packaging).Strong SQL and data wrangling; experience with Spark/Databricks for large‑scale 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:Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related quantitative field.Preferred Certifications:AWS Certified Machine Learning – SpecialtyAWS 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 certificationsWhat We’re Looking ForSelf-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.