Data Scientist

Data Scientist

Req number:

R7503

Employment type:

Full time

Worksite flexibility:

HybridWho we are

CAI is a global services firm with over 9,000 associates worldwide and a yearly revenue of $1.3 billion+. We have over 40 years of excellence in uniting talent and technology to power the possible for our clients, colleagues, and communities. As a privately held company, we have the freedom and focus to do what is right—whatever it takes. Our tailor-made solutions create lasting results across the public and commercial sectors, and we are trailblazers in bringing neurodiversity to the enterprise.

Job Summary

As the Data Scientist, you will be responsible for driving technical strategy, solution ownership, and the development of advanced AI-powered solutions to address complex business challenges.

Job Description

We are looking for a Data Scientist to lead initiatives across machine learning, deep learning, and generative AI domains, delivering impactful solutions aligned with business objectives. This position will be full-time and hybrid.

What You’ll Do

Technical Strategy & Solution Ownership

• Define and drive the technical direction for ML, Deep Learning, LLM, and Generative AI initiatives aligned with business goals

• Own end-to-end solution design decisions, including architecture, modeling approach, and deployment strategy

• Evaluate emerging AI technologies and recommend pragmatic adoption based on feasibility, scalability, risk, and ROI

• Act as a technical authority on trade-offs between model complexity, performance, cost, and interpretability

Advanced Modeling & Applied AI

• Design, build, evaluate, and deploy supervised and unsupervised ML models, deep learning models, and NLP/LLM-based solutions

• Apply strong fundamentals in statistics, experimentation, and validation to ensure robustness and reliability

• Demonstrate judgment in choosing simple vs. complex approaches based on business context

End-to-End ML & MLOps

• Architect and implement production-grade ML pipelines including data ingestion, preprocessing, feature engineering, model training, validation, deployment, and serving

• Partner with Data Engineering and Platform teams to build scalable, cloud-native ML systems in AWS, Azure, or GCP

• Ensure best practices around model versioning, observability, lineage, and reproducibility

• Adhere to data governance, security, privacy, and compliance standards

Data Modeling & Data Architecture

• Design and review logical and physical data models to support analytics and ML workloads

• Influence data architecture decisions to ensure data quality, performance, and reusability

• Collaborate closely with Data Engineering teams on schema design and data readiness for ML

Databricks & Lakehouse Expertise

• Hands-on experience with Databricks and Lakehouse architectures including Delta Lake, Auto Loader & Pipelines, Feature Store, and Unity Catalog

• Optimize ML and data workloads for performance, scalability, and cost efficiency

• Define best practices for collaborative development using notebooks, repos, and CI/CD workflows

Application Development & Model Consumption

• Build ML-powered applications and tools to expose insights and models to users and downstream systems

• Develop applications using frameworks such as Django, FastAPI, Streamlit, or Dash

• Design and implement REST APIs for model inference and integration

• Partner with Engineering teams to ensure applications meet performance, security, and deployment standards

Graph Technologies

• Design and model data in graph databases such as Neo4j, Amazon Neptune, Azure Cosmos DB, or similar platforms

• Build and optimize graph traversal queries for applications like recommendation systems, fraud detection, knowledge graphs, and lineage tracking

• Integrate graph databases with ETL/ELT pipelines, APIs, and cloud data platforms

Technical Leadership & Influence

• Provide technical mentorship through design reviews, code reviews, and experimentation guidance

• Establish best practices, standards, and reusable patterns across data science initiatives

• Act as a trusted advisor to Product, Engineering, and Business stakeholders

• Translate complex technical outputs into clear, decision-focused communication.

What You'll Need

Required:

• Master's degree in Computer Science, Data Science, Machine Learning, Statistics, or a related field

• 12–15 years of experience in Data Science, Applied ML, or AI-driven product development

• Proven track record of owning large-scale, business-critical ML/AI solutions

• Experience working in environments with high ambiguity and cross-functional dependencies

Technical Skills:

• Strong expertise in machine learning, statistical modeling, deep learning, neural architectures, NLP, and Generative AI systems

• Proficiency in Python and SQL

• Experience with TensorFlow, PyTorch, and modern ML frameworks

• Hands-on experience with Databricks including Delta Lake, Feature Store, MLflow, Model Registry, and Model Serving

• Familiarity with cloud environments such as AWS, Azure, or GCP.

Physical Demands

• Ability to safely and successfully perform the essential job functions

• Sedentary work that involves sitting or remaining stationary most of the time with occasional need to move around the office to attend meetings

• Ability to conduct repetitive tasks on a computer, utilizing a mouse, keyboard, and monitor

Reasonable accommodation statement

If you require a reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employment selection process, please direct your inquiries to [email protected] or (888) 824 – 8111.