Data Science Manager (Multiple vacancies)
Duties: Leads projects or workstreams that translate business problems into workable data science solutions, proposing different approaches and overseeing delivery of predictive analytics, models, and algorithms. Develops project plans, including milestones, dates, owners, risks, and contingency plans. Assembles and evaluates for applicability large, complex data sets from clients and external sources, managing the selection criteria and process for identifying the data and necessary data transformations that meet functional business requirements. Builds analytics tools to communicate actionable insights into customer acquisition, customer retention, margin improvement, operational efficiency, and other key business performance metrics. Performs data cleaning/hygiene and quality control, and integrates data from both client internal and external data sources on advanced data science platforms. Summarizes and describes data and data issues. Conducts statistical data analysis, including exploratory data analysis and data mining, and document key insights and findings toward decision-making. Trains, validates, and cross-validates predictive models and machine learning algorithms using state of the art data science techniques and tools. Documents predictive models/machine learning results that can be incorporated into client-deliverable documentation. Assists clients to deploy models and algorithms within their own architecture. Manages data analyst project work.
Salary Range: $139,547-$160,000
Remote work permitted from any U.S. location. Domestic travel 10% of time to unanticipated client sites for meetings and project-related matters.
Apply to jobs@blend360.com w/ subject “Data Science Manager.”
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Requirements: Master’s degree in Statistics, Mathematics, Computer Science, Data Science, Data Analytics, Business Analytics, or a related quantitative field. Two years of related work experience in advanced data science, to include two years of experience with each of the following predictive modeling and statistical analysis; advanced data science software languages and tools; query languages, semi-structured data, and relational databases, including query authoring and tuning; and cloud-based computing and storage platforms.
