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We are seeking a Senior Data Scientist to develop machine learning models, predictive analytics, and AI-driven solutions that improve customer engagement, marketing performance, operational efficiency, and business intelligence. The role partners with Product, Data Engineering, Software Engineering, Analytics, and business stakeholders to deliver production-ready AI solutions.

This is a full-time, salaried, remote position. Candidate must reside within the Continental U.S. Eastern or Central time zones highly preferred, but all are welcome to apply who can support 8:30AM-5:30PM (EST) hours.

Key Responsibilities

• Design, build, train, evaluate, and deploy machine learning models.

• Develop predictive models including churn, propensity, lead scoring, customer lifetime value, recommendation engines, forecasting, and marketing attribution.

• Perform statistical analysis, hypothesis testing, causal inference, and A/B test analysis.

• Build feature engineering and model training pipelines.

• Deploy and monitor production ML models, including model drift detection and retraining.

• Collaborate with Product, Engineering, Analytics, and executive leadership.

• Mentor junior data scientists and establish best practices.

Required Qualifications

• Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related field, Master's or PhD preferred

• 5+ years of experience building and deploying production ML systems, with a track record of measurable business impact

• Strong communication and business problem-solving skills

Technical Skills

• Expert Python and SQL

• Machine Learning: XGBoost, LightGBM, Random Forests, Neural Networks, Deep Learning

• Statistics: Regression, Bayesian methods, hypothesis testing, experimental design, time series, causal inference

• Snowflake, Matillion, dbt, Pandas, Spark, Airflow

• Cloud: Google Cloud (preferred), AWS, or Azure

• MLOps: MLflow, Kubeflow, Vertex AI Pipelines, Feature Stores, CI/CD

• Data quality and observability: Great Expectations, Monte Carlo, or similar frameworks

• LLMs and AI: OpenAI, Gemini, Claude, LangChain, LangGraph, Semantic Kernel, RAG, vector databases

Preferred Experience

• Large-scale customer data platforms

• Marketing analytics and personalization

• Automotive or SaaS industry experience

• Real-time inference and streaming platforms

Success Metrics

• Deliver production-ready ML models with measurable business impact

• Improve prediction accuracy and operational efficiency

• Implement model monitoring and retraining

• Mentor team members and establish Data Science best practices

• Contribute to team capability growth through documentation, code review standards, and mentorship outcomes that raise the overall quality of the Data Science function

COMPENSATION
Compensation commensurate on experience. Participation in company benefit offerings include medical, dental, vision, unlimited paid leave, 401(k) matching, wellness, and more. The expected salary range for this position is $160,000–$190,000 annually. The final offer will be based on several factors, including relevant experience, skills, and qualifications.

NEXT STEPS
If you meet the requirements, and are interested in applying for this role, please complete the online application, be sure to include a current resume and contact information. Local and remote candidates (living within Eastern or Central Time Zone) will be considered. No relocation assistance provided for this position. NO PHONE CALLS PLEASE.

About Team Velocity
Team Velocity is a full-service marketing agency serving the automotive industry, providing integrated marketing solutions to OEMs and dealerships nationwide. We leverage our proprietary Apollo® technology platform to predict consumer behavior, personalize marketing campaigns, and help dealerships drive more sales and service revenue.
Our team members are driven, creative, and collaborative, enjoying a unique culture where innovation and client success are paramount.
Join us in revolutionizing automotive marketing and technology through powerful, data-driven insights, continuous improvement, and an unwavering commitment to reliability.