Description

Overview Doctor shortages are one of the biggest healthcare challenges in America today and as our Lead Data Scientist you’ll be directly working at Barton to solve it. As our Lead Data Scientist you will work closely with Barton leadership to drive commercial growth for the organization by combining your advanced technical skills, consulting, and business acumen. This is a great role for someone who wants to thrive at the intersection of machine learning and business. Your primary focus will be to improve our medical provider (think doctors, dentists, nurses, etc.) recruiting with a mix of external and internal analytics. Typical problems for this role will solve include: Which providers are most likely to work with Barton? Which providers would be the best fit for a posted listing? What is the best time, channel, and message to engage with providers? What is the optimal outreach and engagement strategy for a provider? How many providers should a recruiter work with? What future specialties do we need to start focusing on today for needs tomorrow? And the most exciting part of all of this is that the team is new, so you’ll be leaving your mark on a blank canvas in a high performing company looking to break through to the next level of growth. Responsibilities Partners with business to understand challenges related to provider recruiting and translate business problems into mathematical problems Leads discovery sessions and workshops to get at root causes and ensure we work to provide the right solutions to the right challenges Works closely with Talent Development to develop training and support content to drive sustained engagement with Data Science capabilities before, during, and after launch Conducts retrospective analysis to identify relationships that lead to better recruiting outcomes Develops predictive models that lead to better decision making by business partners Leverages and creates AI capabilities as for personal and colleague use, e.g., train an LLM to help with phone call preparation Collaborate with data engineering, SalesForce, and IT teams to scale capabilities\ Approaches Data Science with a user-centric, product-inspired perspective Mentor junior data scientists Qualifications 6 to 10 years of professional experience in predictive modeling, data science, machine learning Fluency with data science tools, e.g., SQL, Python, R and ability to learn new tools as needed Hands on experience with explainable machine learning, e.g., Shapley Experience building and deploying data science capabilities at scale via standalone applications, e.g., Shiny, Django, Flask, Angular or by embedding in business systems, e.g., SalesForce Proven track record of unstructured problem solving, e.g., “Acme asked me to help them find their ‘best’ customers…” Fluency with both regression and classification problems and proven ability to explain them to nontechnical audiences Experience working with Large Language Models, e.g., ChatGPT, to develop internal capabilities, e.g., training an LLM to recommend products Ability to engage with business audiences at a variety of levels, e.g., this role will be expected to present to our CEO periodically Willingness to work in both back office / heads down as well as front office / interpersonal capacities, e.g., you’d equally enjoy building a model and leading trainings on how to use it

Role and Responsibilities

• Responsibilities Partners with business to understand challenges related to provider recruiting and translate business problems into mathematical problems Leads discovery sessions and workshops to get at root causes and ensure we work to provide the right solutions to the right challenges Works closely with Talent Development to develop training and support content to drive sustained engagement with Data Science capabilities before, during, and after launch Conducts retrospective analysis to identify relationships that lead to better recruiting outcomes Develops predictive models that lead to better decision making by business partners Leverages and creates AI capabilities as for personal and colleague use, e.g., train an LLM to help with phone call preparation Collaborate with data engineering, SalesForce, and IT teams to scale capabilities\ Approaches Data Science with a user-centric, product-inspired perspective Mentor junior data scientists