Data Science Manager

Job Description Directly manage analyst project work and overall performance, including effective career planning; have difficult conversations and deliver constructive feedback with support from senior management. Interview, hire and train new employees. Analyze team KPIs, develop solutions and alternative methods to achieve goals. Build positive and productive relationships with clients for business growth. Understand client needs and customize existing business processes to meet client needs. Promptly address client concerns and professionally manage requests and projects. Work as a strategic partner with leadership teams to support client needs. Work with practice leaders and clients to understand business problems, industry context, data sources, potential risks, and constraints Problem-solve with practice leaders to translate the business problem into a workable Data Science solution; propose different approaches and their pros and cons Work with practice leaders to get stakeholder feedback, get alignment on approaches, deliverables, and roadmaps Develop a project plan including milestones, dates, owners, and risks and contingency plans Create and maintain efficient data pipelines, often within clients’ architecture. Typically, data are from a wide variety of sources, internal and external, and manipulated using SQL, spark, and Cloud big data technologies Assemble large, complex data sets from client and external sources that meet functional business requirements. Build analytics tools to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics. Perform data cleaning/hygiene, data QC, and integrate data from both client internal and external data sources on Advanced Data Science Platform. Be able to summarize and describe data and data issues Conduct statistical data analysis, including exploratory data analysis, data mining, and document key insights and findings toward decision making Train, validate, and cross-validate predictive models and machine learning algorithms using state of the art Data Science techniques and tools Document predictive models/machine learning results that can be incorporated into client-deliverable documentation Assist client to deploy models and algorithms within their own architecture Qualifications: Qualifications MS degree in Statistics, Math, Data Analytics, or a related quantitative field 4+ years Professional experience in Advanced Data Science, such as predictive modeling, statistical analysis, machine learning, text mining, geospatial analytics, time series forecasting, optimization Experience with one or more Advanced Data Science software languages (R, Python, Scala, SAS) Proven ability to deploy machine learning models from the research environment (Jupyter Notebooks) to production via procedural or pipeline approaches Experience with SQL and relational databases, query authoring and tuning as well as working familiarity with a variety of databases including Hadoop/Hive Experience with spark and data-frames in PySpark or Scala Strong problem-solving skills; ability to pivot complex data to answer business questions. Proven ability to visualize data for influencing. Comfortable with cloud-based platforms (AWS, Azure, Google) Experience with Google Analytics, Adobe Analytics, Optimizely a plus Additional Information All your information will be kept confidential according to EEO guidelines.

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