Data Scientist
Tysons, VA, 20770
Reference ID: 31807244
Date Posted: 02/03/2026
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:
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Own the end-to-end data science lifecycle for sales and advertiser conversion models
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Design, build, validate, and deploy predictive and propensity models
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Engineer features from multi-source vendor and internal datasets
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Translate model outputs into actionable business insights for sales stakeholders
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Partner cross-functionally with sales, product, engineering, and analytics teams
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Develop dashboards and reporting to communicate insights to technical and non-technical audiences
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Monitor model performance, drift, and business impact over time
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Support experimentation, A/B testing, and incrementality analysis
Requirements:
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Must-Haves:
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5 years of hands-on data science experience in production environments
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Strong Python skills (pandas, numpy, scikit-learn)
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Advanced SQL for large-scale analytical datasets
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Experience building classification and conversion/propensity models
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Hands-on experience with Snowflake (Snowpark Python and/or ML capabilities)
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Familiarity with AWS-based data science or ML workflows (e.g., SageMaker)
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Experience working in fast-paced, enterprise environments
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Strong communication skills with ability to explain complex models to business stakeholders
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Experience collaborating with distributed or offshore teams
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Nice-to-Haves:
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Media, advertising, sales, or revenue analytics experience
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Experience with MLOps, model deployment, and monitoring pipelines
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Advanced degree in Data Science, Statistics, Machine Learning, or related field
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Experience with experimentation, causal inference, or A/B testing frameworks
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