Lead Data Scientist - Experimentation

State Farm•Dunwoody, GA

11d•Hybrid

About The Position

Being good neighbors – helping people, investing in our communities, and making the world a better place – is who we are at State Farm. It is at the core of how we operate and the reason for our success. Come join a #1 team and do some good! HYBRID: Qualified candidates must live or relocate within a 180-mile radius of a hub location listed below and should plan to spend time working from home and some time working in the office as part of our hybrid work environment. HUB LOCATIONS: Bloomington, IL; Dunwoody, GA; Richardson, TX; or Tempe, AZ SPONSORSHIP: Applicants for this position are required to be eligible to lawfully work in the U.S. immediately; employer will not sponsor applicants for U.S. work authorization (e.g. H-1B visa) for this opportunity. As a data scientist on the Enterprise Experimentation Team, you will help business partners examine new ideas by formulating hypotheses and designing experiments to test those hypotheses, allowing them to accurately and confidently quantify the impact of their strategic decisions. You will serve as a subject matter expert, consultant, and advocate for experimentation. Through this role, you will work to educate others on experimentation and grow the knowledge base of experimentation across the company. Being on the Enterprise Experimentation Team will help promote your professional growth across multiple areas. First, you will enhance your business acumen by collaborating with a variety of business areas across the Enterprise to produce designed experiments and actionable results. Second, you will enrich your technical skills by facing challenges that require you to dig into your analytic toolbox to identify the right solution. Finally, you will strengthen your communication skills through interactions with business partners that require you to articulate statistical concepts in non-technical ways. In this role, you will share your knowledge and work to increase the use of Experimentation for decision making across the Enterprise. Below are some of the tasks you can expect in the typical day of a data scientist: Develop and validate advanced analytic models, designed experiments, and other data-driven solutions Provide input into and/or build datasets to support solution development Collaborate with other team members on scoping solutions and project decision points Present on technical topics to peers, leadership, and other stakeholders, including non-technical business partners Lead/mentor other data scientists, interns, and other technical work teams Make strategic recommendations on data collection, integration, and retention requirements, incorporating business requirements and knowledge of best practices Serve as a peer reviewer to other model development teams Establish and leverage a network of associates with business domain and data expertise Instill a business-oriented mindset that delivers business outcomes for State Farm’s AI/ML portfolio

Requirements

Completed Masters, other advanced degrees, in an analytical field such as statistics, quantitative marketing, experimental psychology, operations research, management science, industrial engineering, etc., with 3+ years of predictive model building experience
Practical work experience with, and understanding of, topics associated with A/B testing and other experimental design concepts for business contexts
Ability to communicate basic statistical concepts (e.g., statistical significance, confidence intervals, regression models) to business partners
Experience building advanced analytic solutions using generalized linear models and at least one of the following: time series analysis, cluster analysis, tree-based algorithms, or neural networks
Experience with at least one statistical programming language: Python, R, or SAS

Nice To Haves

Experience with mixed linear & non-linear model methodologies
Experience with statistics-based experimental designs, causal inference, or difference-in-difference based estimation
Experience with cloud-based environments (e.g., AWS) or Linux
Strong communication skills and the ability to manage multiple, diverse stakeholders across business areas and leadership levels
Experience gathering and interpreting business requirements for designing scalable data science solutions in a regulatory environment (e.g., insurance, healthcare, and finance).

Responsibilities

Help business partners examine new ideas by formulating hypotheses and designing experiments to test those hypotheses, allowing them to accurately and confidently quantify the impact of their strategic decisions.
Serve as a subject matter expert, consultant, and advocate for experimentation.
Educate others on experimentation and grow the knowledge base of experimentation across the company.
Enhance business acumen by collaborating with a variety of business areas across the Enterprise to produce designed experiments and actionable results.
Enrich technical skills by facing challenges that require digging into the analytic toolbox to identify the right solution.
Strengthen communication skills through interactions with business partners that require articulating statistical concepts in non-technical ways.
Develop and validate advanced analytic models, designed experiments, and other data-driven solutions.
Provide input into and/or build datasets to support solution development.
Collaborate with other team members on scoping solutions and project decision points.
Present on technical topics to peers, leadership, and other stakeholders, including non-technical business partners.
Lead/mentor other data scientists, interns, and other technical work teams.
Make strategic recommendations on data collection, integration, and retention requirements, incorporating business requirements and knowledge of best practices.
Serve as a peer reviewer to other model development teams.
Establish and leverage a network of associates with business domain and data expertise.
Instill a business-oriented mindset that delivers business outcomes for State Farm’s AI/ML portfolio.