Position Summary:
As a member of the Analytics team, the Lead Data Scientist 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.
ESSENTIAL FUNCTIONS:
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.
Performs Analyses
- Use a variety of techniques to analyze large data sets ranging from unsupervised machine learning (ex: XGBoost, CatBoost, 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
Presents Results, Supports Change & Measures Outcomes
- 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
REQUIRED QUALIFICATIONS:
- 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 coding machine learning models (XGBoost, Random Forest, Catboost, etc.), time series (ARIMA, VC, etc.), regression and/or segmentation analyses on large data sets
- 3+ years of hands-on SQL experience, specifically to write complex, highly optimized queries across large volumes of structured and unstructured data in preparation for analyses; experience with Snowflake a plus
- Experience presenting to an executive audience, building insightful presentations that use data to create compelling cases for change
- Can quickly extract insights from various forms of data and succinctly guide the business on how to use them
- Expert in statistics including probability theory and sampling, predictive modeling, test vs. control, and the application of these methods to solve real-world problems
- Demonstrated ability to successfully build rapport with stakeholders that do not have analytical expertise, translating complex mathematical concepts into easy-to-understand terms
- Ability to prioritize, multitask, manage multiple complex projects simultaneously, and deliver results within compressed timelines
- A strategic and tactical thinker; strong vision and ability to think outside the box, provide innovative solutions to core business practices, execute, and implement
- Self-starter, very resourceful, a quick learner with a high degree of agency, integrity and ownership
- Ability to work at a fast pace, both independently and as a team, without sacrificing attention to detail
- Must be comfortable hunting for, aggregating, cleaning, and validating data from multiple sources
Preferred Qualifications:
- Master’s degree in quantitative discipline such as engineering, math, statistics, economics or similar discipline
- Experience in portfolio/risk management and/or quantitative analysis of capital assets
- Experience with bureau data
- Direct knowledge or experience with advanced loan or credit scoring and predictive modeling
- Experience working as a data scientist (or equivalent role/function) in a regulated industry such as financial services, healthcare, real estate, etc., where predictive models must undergo stress testing and scrutiny by a 3rd party auditor and abide by SR 11-7 guidelines
- Experience with the AWS computing environments and storage services such as s3
