Lead Data Scientist at The Friedkin Group

LIVING OUR VALUES

All associates are guided by Our Values. Our Values are the unifying foundation of our companies. We strive to ensure that every decision we make and every action we take demonstrates Our Values. We believe that putting Our Values into practice creates lasting benefits for all our associates, shareholders, and the communities in which we live.

JOB SUMMARY

The Lead Data Scientist at The Friedkin Group (TFG) will spearhead the design, development, implementation and maintenance and improvement of advanced data science initiatives across business units, directly aligning with strategic objectives. This role encompasses transforming innovative ideas into real-world solutions through the application of sophisticated analytical techniques such as machine learning, optimization, and cluster analysis.

The incumbent will lead and help to develop a newly formed data-scientists taskforce in delivering impactful analytical solutions, ensuring these innovations are seamlessly embedded into business operations to drive decision-making, enhance operational efficiency, and foster a culture of continuous improvement and innovation. As part of this role the applicant will play a significant part in setting the AI & ML agenda for The Friedkin Group, including working with business units to define potential opportunities, and defining standards and best practice for AI & ML at TFG

ESSENTIAL FUNCTIONS

• Translates business needs into analytics/reporting requirements to support data-driven decisions with required information & explain ability.
• Keep abreast of the latest data science techniques and technologies. Explore and implement innovative solutions to improve data analysis, modeling capabilities, and business outcomes.
• Communicate complex data insights in a clear and effective manner to stakeholders across the organization, including non-technical audiences. Advocate for the importance and value of data-driven decision making.
• Manage use case design and build teams on day-to-day basis, providing guidance and feedback as they develop and operationalize data science models and algorithms to solve complex business problems.
• Ensure analytical insights and products are embedded into business processes.
• Ensure use case models/analytics are supported, maintained, and improved (as needed) post-development and launch.
• Guide and sign off on analytics/modelling approach, model deployment requirements, and quality assurance standards with input from use case teams and business leadership.
• Provide input to the long/term plan for TFGs Data Science team, including key focus areas, talent acquisition, input to technology platforms, and interaction model with the rest of the organization.
• Foster a culture of innovation and continuous improvement and lead the exploration and adoption of new data science technologies and methodologies to contribute to the advancement of TFG’s analytics expertise.
• Work with wide landscape of business and technical stakeholders to proactively identify applicable new technologies and opportunities and detail and communicate how they can deliver measurable business value.
• Own the analytics solution portfolio, including model maintenance and improvements over time.

SUPERVISORY RESPONSIBILITIES

• Directly supervises one or more employees. Carries out responsibilities in accordance with the organization's policies and applicable laws.
• Demonstrated ability to lead and manage data science projects, including, managing workflow and priorities, to ensure timely delivery of projects with high-quality outcomes.
• Proven track record of recruiting, training, and retaining a skilled data science team, identifying talent gaps, and addressing them.

QUALIFICATIONS

• A master’s degree or PhD in Computer Science, Statistics, Applied Mathematics, or a related field, with at least 5 – 7 years’ experience in data science or a similar role.
• Proficient in at least one analytical programming language relevant for data science. Python ecosystem preferred, R will be acceptable, machine learning libraries & frameworks (e.g. TensorFlow, PyTorch, scikit-learn) and familiar with data processing and visualization tools (e.g., SQL, Tableau, Power BI).
• Expertise in advanced analytical techniques (e.g., descriptive statistics, machine learning, optimization, pattern recognition, cluster analysis, etc.)
• Experience with cloud computing environments (AWS, Azure, or GCP) and Data/ML platforms (Databricks, Spark).
• Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, monitoring, and feedback loop.
• Experience in Supervised and Unsupervised Machine Learning including classification, forecasting, anomaly detection, pattern recognition using variety of techniques such as decision trees, regressions, ensemble methods and boosting algorithms.,
• Good understanding of programming best practices, building for re-use and highly automated CI/CD pipelines.

SOFT SKILLS

• Proven track record of leading cross-functional teams to successfully deliver complex data-driven projects.
• Excellent problem-solving and analytical skills, with the ability to translate complex technical details into understandable business insights.
• Sees overall 'picture' and alternative approaches and develop vision of what may be possible.
• Strong interpersonal and communication skills, capable of working effectively with and developing trust from both non-technical and technical counterparts to influence key use case & enterprise decisions.

CERTIFICATES, LICENSES, REGISTRATIONS*

• Relevant certifications such as Microsoft Certified: Azure Data Scientist Associate or AWS Certified Machine Learning are advantageous.

PHYSICAL REQUIREMENTS

The physical requirements described here are representative of those that must be met by an associate to successfully perform the essential functions of the job.  While performing the duties of the job, the associate is required daily to analyze and interpret data, communicate, and remain in a stationary position for a significant amount of the workday; and frequently access, input, and retrieve information from the computer and other office productivity devices.  The associate is regularly required to move about the office and around the corporate campus.  The associate is occasionally required to travel to other sites, including out-of-state, where applicable, for business.  The associate must frequently move up to 10 pounds and occasionally move up to 25 pounds.

WORK ENVIRONMENT

The work environment characteristics described here are representative of those an associate encounters while performing the essential functions of this job.  While the job is generally performed in an office environment, the associate is occasionally exposed to wet and/or humid conditions, areas in which moving mechanical parts, fumes, toxic or caustic chemicals are present, and outside weather conditions.  The noise level in the office environment is typically quiet, but the associate may be occasionally exposed to loud noise levels. 

TRAVEL REQUIRED

Minimal travel is required for this position (up to 20% of the time and on a domestic basis).

The Friedkin Group and its affiliates are equal opportunity employers and maintain drug-free workplaces by conducting pre-employment drug testing.

TOTAL REWARDS
Our Total Rewards package is an integral part of how we recognize our associates’ contributions as well as attract, retain and reward the most qualified employees. We are committed to providing a fair and competitive compensation structure that includes base pay and performance based rewards, where applicable. Compensation is based on various factors including, but not limited to, skill set, experience, qualifications and job-related requirements. Our benefits include medical, dental, and vision along with wellness programs, retirement plans, paid leave and much more! To learn more about these programs and many more, take a tour of our Benefits Page

We are seeking candidates who are legally authorized to work in the United States, without Sponsorship.

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