A Place for Mom

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Data Scientist

Data Scientist

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Since 2000, A Place for Mom has helped over 1 million families move into senior housing and care. A Place for Mom seeks a talented data scientist to:


  • Partner Efficiency and Partnership Growth:  Build prediction algorithms that help us serve families and our senior living community partners more efficiently
  • Marketing Conversion and Efficiency:  Support marketing and sales teams in our efforts to drive continued market share growth and spending efficiency through our marketing efforts focused on families in need of senior housing and care solutions
  • PR / Industry Authority:  Perform and communicate the results of rigorous confirmatory and exploratory analyses that yield insights of interest internally to A Place for Mom, as well as to the senior living industry, and potentially to the public in the form of newsworthy data stories


The ideal candidate has a passion for rigorous end-to-end data analysis (from data wrangling to visualization and presentation), strong skills in applied statistics and machine learning, and excellent communication skills. The data scientist will report to our VP of Brand Marketing as well as support projects across our sales, marketing and partner organizations collaborating across these and our engineering departments, to deliver the following:


Marketing Lead & Sales Optimization Products to Improve Follow-up Efficiency - In collaboration with the Sales Operations team, the Data Scientist will design algorithms that help our Senior Living Advisors follow up with the right families at the right time. This project involves writing prediction algorithms that balance accuracy with human interpretability. It also involves designing experiments to test the impact of competing algorithms on customer success relative to each other and a control group. Going forward, future projects will include all phases of our sales funnel.


Senior Living Cost Index - A Place for Mom built the public-facing Senior Living Cost Index to help families understand the median cost of different types of senior housing and care in over 3,000 cities in America. The tool is driven by a set of hedonic pricing models (based on individual-level senior housing and care transactions plus socio-demographic and geographic data) and repeat-sales models of price growth. The Data Scientist will continuously improve these models, communicating their results to the public and to reporters via press interviews. The Data Scientist will also work with designers to improve the user interface for the tool.


Industry-Facing Data Product - We are using our massive transaction database to build an industry-facing web app and data product that measures and forecasts senior housing and care demand. Currently in alpha testing phase, this product will be continuously improved and expanded by the Data Scientist. Currently, the web app is implemented in Shinyapps.io, and is driven by prediction and forecasting models built in the R programming language. The Data Scientist will also communicate with potential consumers of the data product, including large real estate investment trusts who invest in senior living communities, and our senior living community partners.


Customer Insights - A Place for Mom has recently appended a massive amount of third-party data to our database of consumers who inquire about senior housing and care. The Data Scientist will dig into this third-party data to assess its potential value to the company, whether it be through the development of consumer segmentation models, or the identification of third-party-sourced variables that could improve existing expected revenue and move-in prediction algorithms.


Data Journalism - The Data Scientist will perform some ad hoc analyses to support public relations efforts, and help our content marketers and public relations consultants pitch newsworthy insights gathered through ongoing research projects.


Academic Collaboration - Academics and top-tier research institutions have expressed interest in using our unique data to study the decisions families make about senior housing and care. The Data Scientist will help to foster these collaborative relationships and support third-party researchers in understanding how to use our data effectively. These efforts may include co-authorship on peer-reviewed journal articles.


  • MA/MS in quantitative social science field (e.g., demography, economics, sociology, or equivalent)
  • 2+ years applying statistical and machine-learning methods to solve business problems
  • Strong SQL programming skills
  • Strong R programming skills; experience in the tidyverse a plus; additional experience in Python nice to have but not required
  • Strong fundamentals and intuition in applied statistics for both prediction and explanation
  • Able to build, optimize, and test prediction and forecasting algorithms at scale (e.g., random forest, gradient boosting machines, structural time series models, generalized linear models, ARIMA)
  • Experience using regularization and dimensionality reduction techniques
  • Able to quickly learn and implement experimental and quasi-experimental designs
  • Able to balance analytical rigor against meeting deadlines
  • Survey research design
  • Knowledge of causal analysis methods (e.g., case/control matching and weighting, synthetic cohorts, instrumental variables)
  • Experience using Census data, especially interfaces into the Census API
  • Excellent presentation, writing, and data visualization skills at the executive level


Preferred Skills

  • Experience building simple web interfaces using Shiny or similar products
  • Public relations experience (e.g., interviews with reporters)
  • Strong journalistic/blog-writing skills
  • Experience using cloud-computing solutions (e.g., AWS) to facilitate automation and scaling of data science workflows
  • Experience working with product managers and business stakeholders to build products driven by prediction algorithms and insights from data analysis
  • Interactive web-based visualization skills (e.g., d3.js, plotly, embedded Tableau or Shiny)
  • One or more published peer-reviewed journal articles