Data Science Manager

Job Description

• Ability to provide ethical and positive leadership that motivates direct reports and develops their talent and skillset while achieving results.
• 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.
• 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 businessprobleminto aworkableData Sciencesolution; proposedifferent approachesand theirprosand cons
• Work with practice leaders to get stakeholder feedback, get alignment on approaches, deliverables, and roadmaps
• Develop a project plan including milestones,dates, owners, andrisks andcontingency plans
• Create and maintainefficientdata pipelines, often within clients’architecture. Typically, data are from a wide variety of sources, internal and external, and manipulatedusing SQL, spark,and Cloud big data technologies
• Assemble large, complex data setsfrom client and external sourcesthat 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 tosummarize and describe data and data issues
• Conduct statistical data analysis, including exploratory data analysis, data mining, and document key insights and findingstoward decision making
• Train, validate, and cross-validate predictive models and machine learning algorithms usingstate of the artData Science techniques and tools
• Document predictive models/machine learning results that can be incorporated into client-deliverable documentation
• Assist client todeploymodels and algorithmswithin their own architecture

Qualifications

• MS degree inStatistics, Math,Data Analytics, or a related quantitative field
• 4+ yearsProfessional 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 deploymachine learning models from the research environment (JupyterNotebooks)toproduction viaproceduralor pipelineapproaches
• ExperiencewithSQLandrelational databases, query authoringand tuningas well as working familiarity with a variety of databasesincluding Hadoop/Hive
• Experience with spark and data-frames inPySparkor Scala
• Strongproblem-solvingskills;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.