Senior Data Scientist

Team Velocity Marketing; Virginia (MN); Hybrid; USD 160,000 - 220,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Unlimited paid leave

401(k) matching

Job summary

Team Velocity Marketing is looking for a Senior Data Scientist to develop machine learning models and improve customer engagement. You will collaborate with various departments to deliver AI solutions that enhance marketing performance and operational efficiency.

With a focus on predictive analytics, you will implement model monitoring and retraining strategies while mentoring junior team members. A competitive compensation package is offered, including comprehensive benefits and an expected salary range of $160,000-$220,000 annually.

Qualifications

• 5+ years of experience building and deploying production ML systems.
• Strong communication and business problem-solving skills.
• Experience in marketing analytics and personalization.

Responsibilities

• Design, build, train, evaluate, and deploy machine learning models.
• Develop predictive models for various business metrics.
• Deploy and monitor production ML models.

Skills

Python

SQL

Statistics

Machine Learning

Data Engineering

Education

Bachelor's degree in Computer Science or related field

Master's or PhD preferred

Tools

Snowflake

Google Cloud

Pandas

Spark

Airflow

Job description

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.

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
• 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
• 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

• 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-$220,000 annually. The final offer will be based on several factors, including relevant experience, skills, and qualifications.