Data Scientist Lead

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As a leading property manager of single-family rental homes nationwide, we take great pride in creating an enjoyable living experience for our residents – and an empowering, people-first culture for our team members. That’s why, for two years in a row, our employees have voted Progress a certified Great Place to Work®.

As the demand for professionally managed rental homes continues to grow, so do the opportunities ’re looking for passionate professionals who are ready to grow with us, make a difference and be part of something meaningful.

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As a member of the Analytics team, the Data Scientist Lead will be focused on improving the single-family rental (SFR) customer experience, increasing occupancy rates, and reducing costs on home repairs and renovations. This is a high visibility role with exposure to multiple functions within the organization. Day to day partnerships include working with his/her team and cross-functionally at the senior leadership level to understand Progress’s business challenges and recommend data-driven solutions.

The right candidate is naturally curious, has excellent quantitative modeling and creative problem-solving skills, an ability to partner with stakeholders to drive results, and likes working in a fast paced environment. Additionally, this person must have demonstrated the ability to execute projects end to end with minimal supervision and be able to create a compelling case for change in PowerPoint, articulating technical concepts to non-technical stakeholders at all levels including the C‑suite.

• Manages Projects Independently
• Establish a professional and trusting relationship with stakeholders (Director and VP level)
• Lead and complete projects once given the objective and direction. Hold intake sessions, size projects, design solutions, manage scope and recalibrate work priorities as needed
• Communicate regularly and effectively on project status, clarity of deliverables, and requirements
• Mentor junior members of the team on projects – structure, solution builds, organizing data, etc.
• Use a variety of techniques to analyze large data sets ranging from unsupervised machine learning (ex: XGBoost, Cat Boost, Random Forest) to research-driven root cause. These tools will be used to highlight opportunities with Progress’s key business drivers. For example, likelihood to renew, collectability scoring, predicted costs for renovations, building decision trees to support operating efficiencies for home repairs
• Segment data using techniques such as Clustering (ex: k‑means, KNN) and Decision Trees
• Develop predictive models for these segments; conduct quantitative research and back‑testing to evaluate model assumptions and develop confidence in results
• Develop new analytical approaches to problem‑solving at Progress, driving improvements in the quality, efficiency & rigor of insights delivered to the business; use findings to guide change
• Gather and aggregate data from multiple internal and external sources. Reconcile data with known sources of truth.
• Uncover patterns, identify root causes, and accurately interpret operational and financial impact
• Create compelling stories with data in PowerPoint; present findings to a broad group of cross‑functional leaders (all levels including C‑suite), using data to drive and influence management decisions
• Develop and implement process controls to mitigate business risk and to improve operating efficiency; measure performance of implemented solutions over time

• Bachelor’s Degree required in a mathematics‑based discipline such as engineering, math, statistics, economics or similar discipline;
  Master’s degree preferred
• 10+ years of relevant data science, analytics, predictive analytics or advanced risk modeling with large-scale data and project management experience
• 3+ years of hands‑on Python experience…