Data Science Manager - Marketing

Job Summary

• Utilizes in-depth knowledge and experience to apply advanced analytics and modeling to improve business results. Leverages customer information and behavioral data to influence strategic business decisions while using complex, innovative analytics, multi-variate models, machine learning and data mining technologies.
• Leads less complex projects operationalizing business decisions and works independently, receiving minimal guidance from manager.
• The role mentors, coaches, and trains less experienced team members and supervises direct reports who are individual contributors.

Essential Job Functions

• Utilizes broad knowledge of consumer analytics including retention models, agency economics, and lead optimization in their daily work. Utilizes in-depth knowledge of advanced programing, 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 a 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.
• Succeeds in projects by scoping, defining measures of success, utilizing 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 deve lopment. Relies on emerging skill in employee engagement to promote a high-performance culture where excellence is standard and innovation is actively encouraged.

Physical Actions

• Job is performed in-person at a Farmers office or virtually at an approved alternative work location. The physical work environment is indoors and climate-controlled with adequate lighting and ventilation. Normal and customary distractions include background noise produced by office equipment and chatter among people, as well as interruptions. Frequently sits for prolonged periods of time, up to a full shift. Occasionally moves about the workplace including, navigating stairs, ramps, and level or uneven surfaces. Occasionally moves, pushes, pulls, lifts, carries, and/or places objects or materials weighing up to 25 pounds without assistance. Frequently uses shoulders, arms, hands, and fingers to manipulate equipment, tools, and objects necessary to perform job duties. Frequently performs fine motor tasks such as typing, mousing, or writing, up to a full shift. Rarely performs movements such as bending, stooping, crouching, kneeling, twisting, and reaching overhead or below the knees. Possesses clear vision, with or without correction, to visually read and verify information. Relies on depth perception and peripheral vision to navigate the work environment visually by identifying barriers, changes in terrain and locating objects. Possesses adequate hearing, with or without correction, to communicate with co-workers, respond promptly to auditory signals or alarms, and discern sounds essential for maintaining safety and productivity in the workplace. Jobs in this category require rare, if any, travel.

Education Requirements

• High School Diploma or equivalent required. Masters degree preferred in data science, statistics, mathematics, business analytics or related.

Experience Requirements

• Minimum five years of work experience required in data analysis, statistical or mathematical modeling, or related. Experience in insurance industry preferred.
• Python experience is required.
• CLTV experience is required.
• ML Ops experience preferred.
• Lead routing experience highly preferred.

Additional Qualification