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Director, Data Science

Posted 8 days agoViewed

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💎 Seniority level: Director, 10+ years

📍 Location: United States

💸 Salary: 208000.0 - 235000.0 USD per year

🔍 Industry: Sports gaming

🏢 Company: Underdog Sports

🗣️ Languages: English

⏳ Experience: 10+ years

🪄 Skills: LeadershipPythonSQLETLMachine LearningNumpyPeople ManagementData sciencePandasTensorflowCommunication SkillsAnalytical SkillsMentoringData visualizationTeam managementStrategic thinkingData modelingData analytics

Requirements:
  • 10+ years of experience in data science, with 4+ years in a leadership role.
  • Proven expertise in building and deploying machine learning models, including at least one or more of LTV, churn prediction, MMM, and recommender systems.
  • Strong understanding of personalization techniques and strategies.
  • Proficiency in Python, R, SQL, and modern machine learning frameworks.
  • Ability to lead and inspire a team, fostering a culture of collaboration and professional growth.
  • Strong communication skills with the ability to convey complex ideas to technical and non-technical audiences.
  • A strategic thinker who aligns data science initiatives with business objectives.
Responsibilities:
  • Lead and mentor a team of data scientists, fostering a collaborative and high-performing culture.
  • Develop predictive models, including customer LTV, churn prediction, MMM, and propensity models, ensuring they drive measurable business outcomes.
  • Build and refine recommender systems to personalize CRM communications, promotions, and in-app user experiences.
  • Work closely with engineering teams to productionize models and integrate them into existing workflows.
  • Partner with product, marketing, and CRM teams to design and implement data-driven personalization strategies.
  • Act as a thought partner to stakeholders across the business, translating complex data science problems into actionable insights.
  • Stay informed on industry trends and emerging techniques in data science and machine learning.
  • Promote best practices in model development, validation, and monitoring to ensure accuracy and reliability.
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