Data Scientist
Greenbelt, MD, 20770
Reference ID: 31846215
Date Posted: 02/21/2026
Apply now: Data Scientist, location is Hybrid (Tysons, VA). The start date is ASAP for this permanent position.
Job Title: Data Scientist
Location-Type: Hybrid (2-3 Days A Week On-site - Tysons, VA 22102)
Start Date Is: ASAP
Duration: Permanent/Direct Hire
Annual Base Salary Range: $130k - $140k
Job Description:
Drive predictive modeling efforts to optimize sales outreach and increase advertising revenue, supporting a new sales-focused data science product initiative.
Day-to-Day Responsibilities:
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Own the full data science lifecycle: hypothesis framing, model development, testing, deployment, and impact analysis.
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Build predictive conversion models using advertiser data, sales history, and market signals.
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Engineer features from vendor-sourced, multi-channel advertiser datasets.
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Translate ML outputs into executive-facing dashboards and automated BI reports.
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Develop and validate classification models (e.g., logistic regression, gradient boosting, neural nets).
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Leverage Snowflake ML (e.g., Snowpark, Cortex ML) for scalable model prototyping and deployment.
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Conduct A/B testing and causal inference studies to measure business impact.
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Ensure model governance: tracking, drift detection, retraining, and documentation.
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Partner cross-functionally with sales, marketing, and product teams.
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Mentor peers on responsible AI, experimentation, and predictive analytics.
Requirements:
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Must-Have Skills/Experiences:
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5 years of applied data science experience, ideally in media or advertising.
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Strong Python (pandas, scikit-learn) and advanced SQL skills.
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Hands-on with Snowflake, including Snowpark and/or Cortex ML.
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Experience with model deployment, monitoring, and performance tuning.
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Expertise in classification modeling, especially in customer conversion or lead scoring.
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Ability to interpret and present ML insights to non-technical stakeholders.
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Experience working in enterprise environments and with offshore teams.
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Strong communication and collaboration skills.
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Nice-to-Have Skills/Experiences (NOT required, but a plus!) :
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Advanced degree in Data Science, Machine Learning, or related field.
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Prior experience in revenue-focused analytics (sales or marketing).
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Familiarity with MLOps and model pipeline automation.
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Experience with AWS (especially SageMaker).
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Background in fast-moving, AI-driven organizations.
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