Data Scientist

Apply now: Data Scientist , location is Hybrid . The start date is ASAP for this permanent position.

Job Title: Data Scientist

Location-Type: Hybrid (2–3 days/week onsite, Tysons, VA)

Start Date Is: ASAP

Duration: Permanent

Compensation Range: $130,000 – $140,000 base salary

Job Description:
Data Scientist responsible for building predictive models and data-driven products that help sales teams prioritize leads, optimize outreach, and grow advertising revenue.

Day-to-Day Responsibilities:

• Own the end-to-end data science lifecycle for sales and advertiser conversion models

• Design, build, validate, and deploy predictive and propensity models

• Engineer features from multi-source vendor and internal datasets

• Translate model outputs into actionable business insights for sales stakeholders

• Partner cross-functionally with sales, product, engineering, and analytics teams

• Develop dashboards and reporting to communicate insights to technical and non-technical audiences

• Monitor model performance, drift, and business impact over time

• Support experimentation, A/B testing, and incrementality analysis

Requirements:

• Must-Haves:

  • 5 years of hands-on data science experience in production environments

  • Strong Python skills (pandas, numpy, scikit-learn)

  • Advanced SQL for large-scale analytical datasets

  • Experience building classification and conversion/propensity models

  • Hands-on experience with Snowflake (Snowpark Python and/or ML capabilities)

  • Familiarity with AWS-based data science or ML workflows (e.g., SageMaker)

  • Experience working in fast-paced, enterprise environments

  • Strong communication skills with ability to explain complex models to business stakeholders

  • Experience collaborating with distributed or offshore teams

• Nice-to-Haves:

  • Media, advertising, sales, or revenue analytics experience

  • Experience with MLOps, model deployment, and monitoring pipelines

  • Advanced degree in Data Science, Statistics, Machine Learning, or related field

  • Experience with experimentation, causal inference, or A/B testing frameworks