Data Scientist

Capgemini; Conshohocken (Montgomery County); On-site; USD 103,000 - 161,000; Full time

Benefits offered by this job

Medical benefits

Dental benefits

Vision benefits

Retirement plan

Job summary

Capgemini seeks a seasoned Data Scientist to develop and deploy machine learning models at scale within Group Digital. You will partner with ML engineers, product teams and business stakeholders to build scalable data pipelines, CI/CD workflows, and ETL processes.

The role focuses on personalization, AI methods including LLMs, Agentic AI, and Knowledge Graphs, with a strong emphasis on production readiness and observability.

Qualifications

• 7+ years in Data Science or ML fields.
• Strong Python, SQL, ML frameworks (TF, PyTorch, Scikit-Learn).
• Experience with MLOps tools (MLflow, Kubeflow, Airflow).
• Proficiency with AWS/GCP/cloud data engineering.

Responsibilities

• Develop and deploy ML models and neural networks.
• Build scalable data pipelines and CI/CD for ML workloads.
• Implement LLM-powered and agentic AI solutions.
• Collaborate with ML engineers, product teams and stakeholders.

Skills

Python

SQL

TensorFlow

PyTorch

ML Ops

Cloud (AWS/GCP)

MLOps

Agile/DevOps

Education

Master’s or above in CS/Math/Engineering

Tools

OpenAI Agents SDK

LangGraph

Google Agent Development Kit

Semantic Kernel

LangSmith

Kubernetes

Job description

Contract (1 year)

Published 2 days ago

Python (Pandas, PySpark), SQL, Apache Spark, Spark SQL

aws

SQL

search retrieval

tensor flow

GCP

tensorflow

Job Summary

We are seeking a highly skilled Data Scientist to drive the adoption of algorithmic decision-making at scale within Group Digital. This role will focus on developing and deploying machine learning models, neural networks, support MLOps practices, to enhance personalization, and automation within our digital products.

You will work closely with machine learning engineers, product teams, and business stakeholders to build scalable data pipelines, CI/CD workflows, and ETL processes.

The ideal candidate will have experience in retail, personalization web technologies, and cutting-edge AI methods, including Large Language Models (LLMs), Agentic AI, and Knowledge Graphs.

Key Responsibilities

Develop and optimize predictive and prescriptive models to extract insights and enhance decision-making. Knowledgeable in supervised and unsupervised learning.

Apply deep learning and neural network techniques for classification and profiling, and personalization.

Utilize MLOps to efficiently deploy, monitor, and maintain ML models in production.

Implement and fine-tune Large Language Models (LLMs) and Agentic solutions for automation and user engagement.

Agentic AI Capabilities

Design, build and deploy reliable AI agents capable of planning, reasoning, using tools, maintaining context and completing multi-step business workflows.

Develop single-agent and multi-agent solutions, including specialist agents, supervisor/orchestrator patterns, agent handoffs, workflow routing and parallel task execution.

Integrate agents with enterprise systems, APIs, databases, knowledge bases, cloud services and productivity tools using secure tool-calling patterns and the Model Context Protocol (MCP).

Build retrieval-augmented generation solutions using document ingestion, chunking, embeddings, vector search, reranking, metadata filtering and grounded response generation.

Implement agent memory and state management, including short-term context, long-term memory, session persistence, checkpointing and resumable workflows.

Design harnessing with human-in-the-loop controls for approvals, escalations, exception handling and high-impact decisions.

Establish guardrails for prompt injection, excessive permissions, sensitive-data exposure, unsafe tool execution, hallucinations and unauthorized actions.

Create evaluation frameworks for agent quality, tool-selection accuracy, groundedness, reliability, latency, cost, safety and task completion.

Implement observability for agent traces, tool calls, intermediate state, failures, token usage, latency and business outcomes.

Continuously test and improve agent behaviour through offline evaluation sets, simulation, red-teaming, regression testing and production monitoring.

Work in an Agile/DevOps environment, collaborating with cross-functional teams to drive data-driven innovation.

Promote a data-centric culture by educating teams on the strategic importance of AI and analytics.

Clearly communicate complex methodologies, results, and business insights to both technical and non-technical audiences.

Required Skills & Qualifications

7+ years of experience in Data Science, Machine Learning, or related fields.

Strong expertise in Python, SQL, and modern ML frameworks (TensorFlow, PyTorch, Scikit-Learn).

Experience with MLOps tools (MLflow, Kubeflow, Airflow) for model deployment and monitoring.

Proficiency in cloud platforms (AWS/GCP) and scalable data engineering.

Strong understanding of probability theory, statistics, and experimental design (A/B Testing).

Experience with collaborative software engineering practices (Agile, DevOps).

Preferred Master’s or above degree in Computer Science, Mathematics, Engineering, or related field.

Practical experience developing LLM-powered applications using model APIs, structured outputs, function/tool calling, streaming, multimodal inputs and asynchronous workflows.

Experience with agent-orchestration frameworks such as OpenAI Agents SDK, LangGraph, Google Agent Development Kit, Semantic Kernel or equivalent frameworks.

Experience designing and integrating MCP clients and servers, including tools, resources, prompts, authentication, authorization and secure capability exposure.

Knowledge of RAG and search technologies, including vector databases, hybrid search, embeddings, rerankers, document-processing pipelines and knowledge graphs.

Experience designing and developing recommendation systems, including collaborative filtering, content-based and hybrid approaches, candidate generation, ranking, personalization, feature engineering, and offline/online evaluation through A/B testing.

Strong understanding of information retrieval concepts and search relevance, including indexing, tokenization, query understanding, lexical, vector and hybrid search, embeddings, reranking, relevance tuning, and evaluation metrics such as Precision@k, Recall@k, NDCG and MRR.

Experience with agent state, memory, event-driven workflows, queues, retries, timeouts, idempotency and long-running or resumable processes.

Familiarity with LLM evaluation and observability tools such as LangSmith, OpenTelemetry, Phoenix, Arize, Weights & Biases or equivalent platforms.

Strong understanding of AI security, including prompt-injection defense, data-loss prevention, least privilege tool access, sandboxing, secrets management, audit logging and model-risk controls.

Experience with cloud AI platforms and deployment technologies, such as Azure AI Foundry, AWS Bedrock, Google Vertex AI, Kubernetes, serverless runtimes or equivalent platforms.

Ability to translate business requirements into measurable agent workflows, success criteria, evaluation datasets and production-readiness standards.

Preferred Qualifications

Experience with Knowledge Graphs (e.g. Neo4J) and their integration into AI/ML pipelines.

Hands-on experience in LLMs (e.g. Gemini, ChatGPT, BERT, LLaMA, Claude) and Generative AI technologies.

Background in Retail and Personalization Web Technologies.

Proficiency in business intelligence (BI) tools and data visualization.

Passionate about machine learning, MLOps, knowledge graphs, and cutting-edge AI.

Experience with multi-agent architectures, including hierarchical, planner-executor, reviewer-critic and ensemble patterns.

Experience deploying agents in isolated execution environments or sandboxes.

Familiarity with agent interoperability standards, model routing, fallback strategies, context engineering, prompt versioning and cost optimisation.

Contributions to internal AI platforms, reusable agent components, MCP servers, evaluation frameworks or open-source projects.

Experience in regulated environments, privacy-by-design, model governance and AI risk management.

The pay range that the employer in good faith reasonably expects to pay for this position is $74.74/hour - $116.79/hour. Our offered benefits include medical, dental, vision and retirement benefits. Applications will be accepted on an ongoing basis. Tundra Technical Solutions is among North America’s leading providers of Staffing and Consulting Services. Our success and our clients’ success are built on a foundation of service excellence. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Unincorporated LA County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: client provided property, including hardware (both of which may include data) entrusted to you from theft, loss or damage; return all portable client computer hardware in your possession (including the data contained therein) upon completion of the assignment, and; maintain the confidentiality of client proprietary, confidential, or non-public information. In addition, job duties require access to secure and protected client information technology systems and related data security obligations.