BLEND360 is an award-winning,  new breed Data Science Solutions Company focused on powering exceptional results to our Fortune 500 clients. We are a  growing company—born at the intersection of advanced analytics, data and technology

Summary 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 Lead Data Scientists have the business acumen to apply Data Scientists to many different business models and situations.  

Work independently. We expect the Lead Data Scientists to be excellent communicators with the able to describe complex concepts clearly and concisely. Lead Data Scientists should be able to work independently from gathering requirements, developing roadmaps, and delivering results. 

Teamwork and Leadership: We work as a team and Lead Data Scientists 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. 

Responsibilities 

• 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 program into a solvable Data Science problem; 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 plans 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 / non-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: 

• PHD degree in Statistics, Math, Data Analytics, or a related quantitative field 

• 5+ years Professional experience in Advanced Data Science, such as predictive modeling, statistical analysis, machine learning, text mining, geospatial analytics, time series forecasting, optimization 

• Optimization Expertise

• 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 (SQL) 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 

• Experience in digital marketing a plus