Staff Data Scientist

Key Skills

Aartificial intelligence, Algorithms, Data Mining, Data science techniques, Data Visualization, Deep Learning, Machine learning techniques, MLOps tools, NoSQL, Python Programming, PyTorch, R Programming, SPARK Programming, SQL

Job Description

Fanatics is building a leading global digital sports platform. The company ignites the passions of global sports fans and maximizes the presence and reach for hundreds of sports partners globally by offering innovative products and services across Fanatics Commerce, Fanatics Collectibles, and Fanatics Betting & Gaming, allowing sports fans to Buy, Collect and Bet. Through the Fanatics platform, sports fans can buy licensed fan gear, jerseys, lifestyle and streetwear products, headwear, and hardgoods; collect physical and digital trading cards, sports memorabilia, and other digital assets; and bet as the company builds its Sportsbook and iGaming platform. Fanatics has an established database of over 100 million global sports fans, a global partner network with over 900 sports properties, including major national and international professional sports leagues, teams, players associations, athletes, celebrities, colleges, and college conferences, and over 2,000 retail locations, including its Lids retail business stores. 

As a market leader with more than 18,000 employees, and hundreds of partners, suppliers, and vendors worldwide, we take responsibility for driving toward more ethical and sustainable practices. We are committed to building an inclusive Fanatics community, reflecting and representing society at every level of the business, including our employees, vendors, partners and fans. Fanatics is also dedicated to making a positive impact in the communities where we all live, work, and play through strategic philanthropic initiatives.

We’re hiring a Staff Data Scientist for our Data Engineering, Science, and Analytics team. If you excel at using data science to tackle business issues, this role could be perfect for you. As part of our team, you’ll get the chance to apply your data science skills to generate business growth. You’ll extract vital insights from diverse data sources, employ machine learning algorithms, and apply advanced statistical models. Your findings will be translated into business actions. Expect a daily array of new challenges and captivating projects that will leverage your interests and strengths.

Responsibilities:

• Team up with colleagues from engineering, operations, marketing, and finance to understand business needs and scope data science projects.
• Wrangle, process, cleanse, validate, and enrich data from different sources and prepare it for analysis.
• Assist in crafting data-informed business strategy and roadmaps.
• Convert data requirements into data product requirements, evaluate technologies, and identify opportunities to innovate and improve our data science capabilities.
• Apply innovative problem-solving skills to dissect data and build machine learning / statistical models to solve business problems from different perspectives.
• Work with engineers to create services that can ingest and supply data to and from both internal and external sources and ensure data quality and timeliness
• Lead the development of data visualization and dashboards to help the organization monitor performance, generate insights, and continuously improve user experience
• Establish playbooks to drive process and consistent outcomes
• Participating in the full life cycle of model development, which spans from business problem discovery, data discovery to model deployment and monitoring.

Qualifications:

• Master’s or Doctoral degree in Computer Science (with a focus on AI, Machine Learning, Data Mining), Statistics, Econometrics, Physics, or other quantitative disciplines that require processing and modeling data at a large scale.
• 5+ years of professional experience as a data scientist.
• Proficient in Python, SQL, Spark, the associated Python and Spark packages commonly used by data scientists, and Deep Learning libraries, such as PyTorch.
• Proficient in wrangle and analyze data with complex relationships and time scale.
• Strong understanding of and practical experience in a wide range of machine learning algorithms and statistical models.
• Strong understanding of and practical experience in using generative AI models.
• Experience in using data visualization and dashboard tools.
• Working experience in cloud-native technology.
• Experience working with large structured and unstructured datasets stored in relational and NOSQL databases.
• Out-of-the-box thinker and problem solver who can turn ambiguous business problems into clear data-driven solutions that deliver meaningful business impacts.
• Excellent organizational skills, verbal and written communication skills, and presentation skills.

Pluses:

• Doctoral degree in computer science with a strong focus on deep learning and AI.
• Proficient in other languages, such as R, used in data science.
• Knowledge of data engineering and MLOps.
• Certificates earned in AI, data mining, machine learning, deep learning, statistical modeling, cloud computing, data engineering, and MLOps.