- July 28, 2020
- Posted by: Melissa Soriano
The Data Science Leader will develop and manage data science empowering smart decision-making capabilities through data and analytical solutions. Responsibilities include, but are not limited to, supporting data governance for critical business data, determining and implementing appropriate tools, technique and methodologies to extract data that produces meaningful results, providing strategic guidance and direction for analytical efforts and in conjunction with senior leadership, help the entire executive team continue to deepen understanding of opportunities in our business, and create strategies and tactics to capture those opportunities, across all departments. Additionally, this may role may manage staff.
- Working knowledge of a range of data management and data science approaches, tools, techniques, and methodologies in data and analytics development management.
- Effective interpersonal, oral and written communication skills with the ability to interact with all levels of people within and outside the organization.
- Expert skills with regression models (linear, logistic, stepwise, multivariate, etc.).
- Excellent time management and decision-making skills.
- Ability to disseminate significant amounts of data into clear and understandable information to use in business decisions.
- Technical knowledge of data management, statistics and utilization of statistical packages for analyzing datasets, predictive modeling and visualization techniques.
- Expert level knowledge and execution capabilities of common data structures, languages, and tools (e.g. SQL, Python, R, Storm, Kafka, Neo4J).
- Expert level knowledge in BI reporting and database management platforms including Tableau, MicroStrategy, Redshift, DynamoDB, Hadoop, PowerBI, or Oracle.
- Develop business/data requirements; business definitions of data; data flows; data mappings and lineage based on "best practices"; specifications to model and implement data collection, optimization, solution delivery, data analytics and other strategies that optimize statistical efficiency and quality.
- Design and launch innovative and complex analytic models, utilizing a blend of contemporary and traditional data mining techniques, which, when applied to both structured and unstructured data sets, drive insights and benefits not otherwise apparent.
- Lead data analysis efforts, directing as necessary, other business and/or information analysts across one or more concurrent delivery efforts to understand and assess the availability, quality, and optimal alignment of data to meet business needs across operations and solutions.
- Oversee the interpretation of results from multiple sources using a variety of techniques, ranging from simple data aggregation via statistical analysis to complex data mining.
- Develop data understanding, narratives, and data flows using methods and tools, to translate for data scientists, engineers, designers, and other product managers to shape and drive solution delivery.
- Perform data profiling and assessment to interpret data; analyze results using statistical techniques to provide ongoing informational, descriptive, and diagnostic evaluations and data stories; identify, analyze, and interpret trends/patterns in complex data sets; and work with management to prioritize business and information needs.
- Present recommendations on an ongoing basis to management and trading staff as to trends impacting the grain industry and price relationships.
- Participate in applicable strategic planning meetings relative to capital investments, annual budgeting, and strategic relationship management.