Data Science Architect | 9 to 14 years | Gurgaon

Capgemini Engineering; Gurugram District; On-site; INR 2,400,000 - 4,200,000; Full time

Job summary

Capgemini Engineering in India Gurugram seeks a senior data science leader to own the end-to-end data science lifecycle across multi-industry engagements, from presales to productionization. You will shape AI/ML platform architecture, guide model development and ensure governance in large-scale programs.

You will partner with data engineering, define roadmaps toward autonomous ML operations, mentor delivery leads, and drive measurable business impact using trusted frameworks and best practices.

Qualifications

• Experience designing and deploying analytics and AI solutions across multiple industries.
• Strong programming and statistical modeling skills are required.
• Ability to lead ML lifecycle from problem framing to production deployment.

Responsibilities

• Lead presales engagements - AI/ML solution architecture and effort estimation for clients.
• Own end-to-end data science lifecycle - framing, EDA, feature engineering, modeling, validation, deployment.
• Architect greenfield data/ML platforms and select vendor tools.
• Define roadmap toward autonomous ML operations with automated retraining and drift detection.
• Lead platform modernization and migrations with minimal business disruption.
• Partner with data engineering to ensure pipeline quality, governance and lineage.

Skills

Python

R

SQL

Machine Learning

Data Modeling

Statistical Analysis

Education

MTech/MS in CS/Math/Statistics

Tools

TensorFlow

PyTorch

Scikit-learn

MLflow

Kubeflow

Databricks

Spark

Hadoop

AWS

Azure

GCP

Power BI

Tableau

LangChain

Job description

At Capgemini Engineering, the world leader in engineering services, we bring together a global team of engineers, scientists, and architects to help the world’s mostinnovative companies unleash their potential. From autonomous cars to life-saving robots, our digital and software technology experts think outside the box as theyprovide unique R&D and engineering services across all industries. Join us for a career full of opportunities. Where you can make a difference. Where no two days arethe same.

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

Tools / Technologies

• Python
• R
• SQL
• Scikit-learn
• TensorFlow
• PyTorch
• XGBoost

MLOps & Deployment

• MLflow
• Kubeflow
• Amazon SageMaker
• Azure ML
• Vertex AI

Data Platforms

• Databricks
• Snowflake
• Apache Spark
• Hadoop

Cloud

• AWS
• Azure
• GCP

Visualization

• Power BI
• Tableau

Generative AI

• LangChain
• Azure OpenAI/OpenAI
• RAG frameworks

Frameworks & Program Delivery

• CRISP-DM
• Agile/SAFe
• TOGAF
• MS Project
• Jira
• Confluence
• PMP/Prince2

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.

Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.