Full-Time Data Science Manager

Job Description

With focus on communication: Data Scientists at Blend360 work with business leaders to solve our clients’ business challenges. Here at Blend360 we work with clients in marketing, revenue management, customer service, inventory management and many other aspects of modern business. Our Data Science leaders have the business acumen to apply Data Science to many different business models and situations. 
 We expect the Data Science Directors to be excellent communicators with the ability to describe complex concepts clearly and concisely. They should be able to work independently in gathering requirements, developing roadmaps, and delivering results.
  

Teamwork and Leadership: We work as a team and Data Science Directors lead both by mentoring or managing Data Scientists as well as leading by example.
 

Technical know-how: Our Data Scientists have a broad knowledge of a variety of data and mathematical solutions. Our work includes statistical analyses, predictive modeling, machine learning, and experimental design. We evaluate different sources of data, discover patterns hidden within raw data, create insightful variables, and develop competing models with different machine learning algorithms. We validate and cross-validate our recommendations to make sure our recommendations will perform well over time.
 

Conclusion: If you love to solve difficult problems and deliver results; if you like to learn new things and apply innovative, state-of-the-art methodology, join us at Blend360. As our new Data Science Director you will:

• Provide ethical and positive leadership that motivates direct reports and develops their talent and skillset while achieving results.
• Run experiments in different marketing channels. Design and analyze experiments. Hands on a must. Lead a team of Data Scientists to design, monitor and evaluate marketing experiments intended to validate MMM assumptions.
• 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.
• Develop and implement optimization models to help our clients improve decision-making process, enhance efficiency and drive business growth. 
• 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 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

• MS degree in Statistics, Math, Data Analytics, or a related quantitative field
• Expert in Experimentation and measurement in marketing. Knowledge of how to run experiments in different marketing channels. Sufficient statistical knowledge to design and analyze experiments. Hands on a must. Data science team to design, monitor and evaluate marketing experiments intended to validate MMM assumptions.
• 3+ years Professional experience in Advanced Data Science, such as predictive modeling, statistical analysis, machine learning, text mining, geospatial analytics, time series forecasting, optimization
• Experience in the Pharma domain
• 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