Data Science Manager

Data Science Manager

Requirements

• 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)

Responsibilities

• 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.

Preferred Qualifications

• Experience with Google Analytics, Adobe Analytics, Optimizely a plus