Apply advanced analytics, machine learning, deep learning, and AI methodologies to build predictive models and intelligent automation systems that deliver measurable business impact.
Own the full model lifecycle: data exploration, modeling, validation, deployment, monitoring, and continual improvement.
Diagnose and resolve model performance issues; implement scalable, reliable processes for ongoing model operations.
Use strong programming skills to prepare complex datasets (structured, semi‑structured, unstructured) and build reproducible pipelines.
Translate complex analytical findings into clear, actionable insights that influence decision‑making at all levels.
Present recommendations in a compelling narrative that resonates with both technical and non‑technical stakeholders.
Lead discovery sessions to clarify business needs, define success metrics, and design analytical approaches that support business strategy.
Drive innovation in Data & AI through new techniques, experimentation frameworks, and reusable analytical assets.
Establish and promote best practices in model development, MLOps, governance, and data quality.
Stay current on emerging trends, regulations, and technologies; provide guidance on how to leverage them within the organization.
Serve as a subject matter expert for stakeholders across the enterprise.
Partner with cross‑functional teams—including Engineering, Product, and Business—to deliver solutions that scale.
Lead or contribute to strategic initiatives, roadmaps, and business cases that support the organization’s data and AI vision.
Mentor and guide other data scientists; contribute to internal training, tools, and knowledge‑sharing.
Manage complex, ambiguous problems by structuring analytical approaches and selecting appropriate modeling techniques.
Lead change management and communication efforts associated with analytics initiatives.
Implement processes and controls that ensure high‑quality, consistent delivery of analytical work.

Requirements

7+ years of relevant experience in data science, machine learning, or applied analytics.
Advanced degree (PhD preferred) in Computer Science, Engineering, Statistics, Mathematics, Physics, or related quantitative field—or equivalent experience.
Proven track record of shipping models to production and maintaining them at scale.
Expertise in machine learning and deep learning algorithms and frameworks.
Proficiency in Python and SQL; experience with R, SAS, or other analytical tools is a plus.
Experience with distributed computing technologies (Hive, Hadoop, Spark) and cloud ML platforms (AWS SageMaker, Azure ML, etc.).
Strong communication, storytelling, and influence skills; able to drive alignment across diverse groups.
Demonstrated ability to operate in ambiguity, make data-driven decisions, and solve complex, open-ended problems.
Experienced collaborator with deep cross-functional partnership skills and ownership mindset.

Benefits

health insurance
tuition reimbursement
accident and life insurance
retirement savings plans

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningdeep learningpredictive modelingdata explorationmodel validationmodel deploymentPythonSQLRSAS

Soft Skills

communicationstorytellinginfluencecollaborationproblem-solvingleadershipmentoringinnovationdecision-makingchange management

Certifications

PhD in Computer SciencePhD in EngineeringPhD in StatisticsPhD in MathematicsPhD in Physics