À propos
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:
Own the full data science lifecycle: hypothesis framing, model development, testing, deployment, and impact analysis. Build predictive conversion models using advertiser data, sales history, and market signals. Engineer features from vendor-sourced, multi-channel advertiser datasets. Translate ML outputs into executive-facing dashboards and automated BI reports. Develop and validate classification models (e.g., logistic regression, gradient boosting, neural nets). Leverage Snowflake ML (e.g., Snowpark, Cortex ML) for scalable model prototyping and deployment. Conduct A/B testing and causal inference studies to measure business impact. Ensure model governance: tracking, drift detection, retraining, and documentation. Partner cross-functionally with sales, marketing, and product teams. Mentor peers on responsible AI, experimentation, and predictive analytics. Requirements:
Must-Have Skills/Experiences: 5+ years of applied data science experience, ideally in media or advertising. Strong Python (pandas, scikit-learn) and advanced SQL skills. Hands-on with Snowflake, including Snowpark and/or Cortex ML. Experience with model deployment, monitoring, and performance tuning. Expertise in classification modeling, especially in customer conversion or lead scoring. Ability to interpret and present ML insights to non-technical stakeholders. Experience working in enterprise environments and with offshore teams. Strong communication and collaboration skills.
Nice-to-Have Skills/Experiences
(NOT required, but a plus!)
: Advanced degree in Data Science, Machine Learning, or related field. Prior experience in revenue-focused analytics (sales or marketing). Familiarity with MLOps and model pipeline automation. Experience with AWS (especially SageMaker). Background in fast-moving, AI-driven organizations.
Compétences linguistiques
- English
Avis aux utilisateurs
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