Lead Data Scientist - Spectrum Reach
Spectrum
Summary:
Spectrum Reach () grows businesses of all sizes with custom, multiscreen advertising solutions, backed by the power of TV, data, innovation, community experts, and unforgettable creative.
Responsible for leading the development of data-driven solutions for Spectrum Reach’s advertising sales business. Utilizes analytical, statistical, and programming skills to clean, aggregate, and analyze large data sets and interpret results. This position requires a strong command of statistical techniques machine learning algorithms, and big data technologies. The ideal candidate will possess a demonstrated practical ability to determine where to invest time, and depending on the assignment, to build sustainable predictive models and services or to synthesize actionable findings and present findings to audiences with diverse agendas and varying levels of technical expertise.
- Plan and lead the complete analytics life-cycle for problem solving, including: requirements gathering, problem formulation, data grooming, data exploration, model prototyping, model validation, and algorithm productionalization.
- Leverage knowledge in analytical and statistical algorithms to help stakeholders explore methods to improve their business. Utilize experience and feedback to recommend options during the development/research/analysis phase.
- Lead large-scale exploratory data analyses for new data sources or new uses for existing data sources. Establish links across existing data sources and find new, interesting data correlations.
- Plan effectively to ensure analytics products are flexible, modular, and both leverage and contribute to the team’s existing reusable code base.
- Exercise thought leadership and discretion in tailoring the tools, approaches, and data used to meet the needs of the particular problem.
- Responsible for interpretation of results – for both causal inferences and predictive effectiveness.
- Synthesize appropriate recommendations for action and changes.
- Present findings, suggested actions and changes to a broad audience, and manage follow-ups and execution.
- Help teach and explain techniques and tools used to a broad set of business-intelligence, data, and analytics professionals with varied backgrounds.
- Create project plans to help team achieve defined project goals within deadlines; proactively communicate status and escalate issues as needed.
- Mentor and train junior team members as well as assist leadership in setting strategic vision.
- Perform other duties as assigned
- Demonstrated mastery and experience with languages such as R and/or Python (including relevant packages) in support of advanced analytics and AI/ML
- Demonstrated experience with SQL, large data sets and the tools to efficiently obtain, transform, and store large volumes at scale. Firm understanding of data architecture and data warehousing techniques
- Experience and proficiency with commercial cloud platforms such as AWS, Azure, GCP
- Demonstrated experience and solid theoretical foundation of the properties of the major families of machine learning models, including regression, classification, clustering, neural networks, and large language models
- Experience with large scale multi-series time series forecasting
- Command of advanced mathematical concepts including calculus, PDEs, probability, and statistics, and the ability to independently learn any necessary additional concepts
- Effective synthesis and presentation skills
- Ability to communicate results and recommendations to a wide variety of audiences
- Demonstrated ability and desire to continually expand skill set, and learn from and teach others
- Experience in the advertising sales, cable and/or telecommunications industry
- Advanced Python knowledge and deep experience in the AI/ML ecosystem surrounding Python
- Experience in Operations-research/optimization, in particular mixed-integer programming, multi-objective optimization, CPLEX and Gurobi solver configuration and performance tuning
- Experience in AWS ecosystem (including EMR, Glue, Athena, SageMaker, Bedrock)
- Experience in Snowflake architecture, resource optimization, and performance tuning
- Experience in distributed computing paradigms and parallel processing, including Spark optimization techniques and performance tuning
- Experience in MLOps, including end-to-end ML pipelines with automated training, validation, and deployment, model registry systems for versioning and governance
- Bachelor's degree in computer science, statistics, operations research and/or equivalent combination of education and experience
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