Data Science Manager - Marketing

Farmers Insurance Careers

4d•$108,375 - $205,750•Hybrid

About The Position

Farmers is seeking a Data Science Manager for Marketing to leverage advanced analytics and modeling to improve business results. This role will utilize customer information and behavioral data to influence strategic business decisions using complex, innovative analytics, multi-variate models, machine learning, and data mining technologies. The manager will lead less complex projects, operationalize business decisions, and work independently with minimal guidance. A key aspect of this role is mentoring, coaching, and training less experienced team members, as well as supervising direct reports who are individual contributors. Farmers offers a results-driven, high-performance culture that thrives on creativity, accountability, and bold solutions, fostering growth for both the organization and its employees. The company is an award-winning, equal opportunity employer committed to making a real difference in people's lives by delivering peace of mind when it matters most.

Requirements

Minimum five years of work experience required in data analysis, statistical or mathematical modeling, or related.
Python experience is required.
CLTV experience is required.
Strong verbal communication and listening skills.
Strong storytelling skills with ability to communicate complex data insights clearly to technical and non-technical audiences.
Demonstrated written communication skills.
Demonstrated problem solving skills.
Effective interpersonal skills.
Ability to influence internal and/or external constituents.
Demonstrated analytical skills.
Possesses strong technical aptitude.
Seeks to acquire knowledge in area of specialty.
Proficient in Microsoft Office Suite.
Demonstrated time management and priority setting skills.
Able to handle shifting priorities.
Strong proficiency working on large-scale structured and unstructured multidimensional data using in-depth knowledge of open-source cloud-enabled analytical programming languages.
Strong ability to consult on data extraction, data manipulation and data design for statistical, modeling and monitoring needs.
In-depth knowledge of data analysis, manipulation tools (SQL, Python, SAS, R, and/or Snowflake) and cloud computing services (AWS).
In-depth knowledge of data visualization tools (example, Tableau, Power BI).
Strong proficiency in using explanatory, diagnostic, and inferential techniques such as experimental design, hypothesis testing, clustering analysis, time series and other statistical modeling algorithms with the ability to decide the appropriate methodology for the purpose.
Strong proficiency in predictive and prescriptive modeling using advanced machine learning and deep learning techniques.
In-depth knowledge of ML/AI model deployment best practices.
Able to adapt quickly to new technologies.
In-depth knowledge of coding standards and version control (Git).
Working knowledge of data ethics and data privacy.

Nice To Haves

Experience in insurance industry preferred.
ML Ops experience preferred.
Lead routing experience highly preferred.
Masters degree preferred in data science, statistics, mathematics, business analytics or related.

Responsibilities

Utilizes broad knowledge of consumer analytics including retention models, agency economics, and lead optimization in their daily work.
Utilizes in-depth knowledge of advanced programming, complex ETL, and specialized modeling methods and execute projects.
Demonstrates clean reusable code and effective documentation, encourages others to do the same.
Acts as the main contributor to multiple phases of a data science project (ideation, experiment design, EDA, feature engineering, model building, deployment, etc.).
Utilizes a strong sense of ownership and contributes to multiple tasks simultaneously.
Executes on complex and often vague business challenges involving data science.
Scopes projects, defines measures of success, utilizes a data science vision for project success, and accomplishes successfully with some vague timelines.
Executes on broad projects independently, with a sense of urgency.
Partners closely with IT, business, and data management/engineering teams to understand, utilize, and improve our data infrastructure.
Advises on difficult matters and serves as an objective and transparent partner to drive fact-based decision making and a measures of success culture.
Develops presentations and presents to leadership.
Regularly communicates complex technical material understandable to non-technical associates.
Manages complex model deployments via established MLOps techniques.
Works with analytics and IT teams to deploy models/rules in various platforms and support testing of new solutions.
Helps to steer MLOps environment improvements.
Supervises, guides, coaches, and manages the performance of direct reports.
Delivers regular feedback and supports professional development.
Relies on emerging skill in employee engagement to promote a high-performance culture where excellence is standard and innovation is actively encouraged.