Data Science Architect | 9 to 14 years | Gurgaon

Job Description

Owns the data science lifecycle end-to-end across multi-industry engagements - presales and opportunity shaping, greenfield ML/AI platform architecture, model development and productionization, and program governance through steady-state delivery, with a roadmap toward autonomous, self-optimizing AI/ML operations.

Key Responsibilities

. Lead presales engagements - RFP/RFI response, AI/ML solution architecture, effort estimation and commercial shaping - and present win themes to executive level stakeholders.

. Own the end-to-end data science lifecycle - problem framing, exploratory data analysis, feature engineering, model development, validation and deployment.

. Architect greenfield data and ML platforms (cloud-native data lakes/lakehouses, feature stores, MLOps pipelines), including vendor and tooling selection.

. Define the roadmap toward autonomous, self-optimizing ML operations - automated retraining, drift detection and closed-loop model monitoring.

. Lead large-scale brownfield data and analytics platform modernization and legacy-to-target migrations with minimal business disruption.

. Partner with data engineering to ensure pipeline quality, governance and lineage, while personally owning model design, validation and business impact.

. Own delivery governance for multi-year, multi-workstream AI/ML transformation programs - scope, schedule, risk, quality and financials.

. Act as single technical point of accountability across design, build, deploy and operate phases, coordinating data engineering, MLOps and business teams.

. Establish and track model and program KPIs (accuracy, drift, business impact), steering committee reporting and executive dashboards.

. Mentor data science/delivery leads and build reusable accelerators, ML frameworks and playbooks across engagements.

Technical Skills & Tools

Primary Skills

Data Science & ML # Experience in designing and deploying advanced analytics and AI solutions using traditional Machine Learning techniques including Classification, Regression, Clustering, Recommendation Systems, Anomaly Detection, Time Series Forecasting, and Reinforcement Learning.S

tatistics # Deep understanding of statistical concepts such as Probability Theory, Hypothesis Testing, Confidence Intervals, Bayesian Statistics, A/B Testing, Experimental Design, Correlation Analysis, Multivariate Statistics, Sampling Techniques, and Predictive

Data Modelling # Modeling. Ability to evaluate data quality, identify bias and fairness concerns, perform causal inference, and develop explainable AI solutions using industry-standard methodologies.

Big Data # Experienced in working with large-scale datasets and Big Data technologies including Spark, Hadoop, Databricks, Kafka, and distributed computing frameworks. Proficient in Python, SQL, and modern data science ecosystems.Deep Learning & GenAI#Deep Learning, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Prompt Engineering, Vector Databases, Agentic AI frameworks, and MLOps practices for enterprise-scale AI deployments. Demonstrated ability to translate complex business problems into data-driven solutions while ensuring Responsible AI, model governance, transparency, and measurable business outcomes.