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About This Role
Conshohocken, PA, United States (On-site) Contract (1 year) Published 6 hours ago Python (Pandas, PySpark), SQL, Apache Spark, Spark SQL aws SQL search retrieval tensor flow GCP tensorflow Python, PyTorch, OpenCV, Azure ML SDK 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
Machine Learning & AI Development
- Develop and optimize predictive and prescriptive models to extract insights and enhance decision-making. Knowledgeable in supervised and unsupervised learning.
- 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.
Collaboration & Agile Development
- 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.
- Strong engineering experience in Python, TypeScript/JavaScript or Java, including API development, testing, version control, CI/CD and cloud deployment.
- 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.
- Experience building voice, vision, document-understanding or computer-use agents.
- 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 $39.86/hour - $62.29/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.
Salary Context
This $81K-$128K range is in the lower quartile for Data Scientist roles in our dataset (median: $161K across 138 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Capgemini, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,503 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($105K) sits 45% below the category median. Disclosed range: $81K to $128K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Capgemini AI Hiring
Capgemini has 32 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, Data Scientist, AI Agent Developer. Positions span Atlanta, GA, US, Dallas, TX, US, Conshohocken, PA, US. Compensation range: $85K - $400K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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