Manager, Data Science

Duties: Tech. Problem Formulation: Requires knowledge of Analytics/big data analytics / automation techniques and methods; Business understanding; Precedence and use cases; Business requirements and insights. To analyze the business problem within one’s discipline and questions assumptions to help the business identify the root cause. Identify and recommend approach to resolve the business problem to create effective technology focused solutions. Set relevant deliverables based on the established success criteria and define key metrics to measure progress and effectiveness of the solution. Quantify business impact. Understanding Business Context: Requires knowledge of Industry and environmental factors; Common business vernacular; Business practices across two or more domains such as product, finance, marketing, sales, technology, business systems, and human resources and in-depth knowledge of related practices; Directly relevant business metrics and business areas. To Provide recommendations to business stakeholders to solve complex business issues. Develop business cases for projects with a projected return on investment or cost savings. Translate business requirements into projects, activities, and tasks and aligns to overall business strategy and develops domain specific artifact. Serve as an interpreter and conduit to connect business needs with tangible solutions and results. Identify and recommend relevant business insights pertaining to their area of work. Data Source Identification: Requires knowledge of Functional business domain and scenarios; Categories of data and where it is held; Business data requirements; Database technologies and distributed datastores (e.g. SQL, NoSQL); Data Quality; Existing business systems and processes, including the key drivers and measures of success. To understand the priority order of requirements and service level agreements. Define and identify the most suitable sources for required data that is fit for purpose, referring to external sources as required. Perform initial data quality checks on the extracted data. Review the deliverables of junior associates and provides guidance on data source and quality. Analytical Modeling: Requires knowledge of feature relevance and selection; Exploratory data analysis methods and techniques; Advanced statistical methods and best-practice advanced modelling techniques (e.g., graphical models, Bayesian inference, basic level of NLP, Vision, neural networks, SVM, Random Forest etc.); Multivariate calculus; Statistical models behind standard ML models; Advanced excel techniques and Programming languages like R/Python; Basic classical optimization techniques (e.g., Newton-Rapson methods, Gradient descent); Numerical methods of optimization (e.g. Linear Programming, Integer Programming, Quadratic Programming, etc.) To select appropriate modeling techniques for complex problems with large scale, multiple structured and unstructured data sets. Select and develop variables and features iteratively based on model responses in collaboration with the business. Conducts exploratory data analysis activities (for example, basic statistical analysis, hypothesis testing, statistical inferences) on available data. Identify dimensions and designs of experiments and create test and learn frameworks. Interpret data to identify trends to go across future data sets. Create continuous, online model learning along with iterative model enhancements. The role will also supervise: 1: Staff Data Scientist, 1: Data Scientist III, 1: Senior, Data Scientist, 1: Senior Manager, Advanced Analytics.

Minimum education and experience required: Bachelor’s degree or the equivalent in Computer Science, Statistics, Analytics or related field plus 5 years of progressively responsible post-baccalaureate experience in analytics or related area; OR Master’s degree or the equivalent in Computer Science, Statistics, Analytics or related field plus 3 years of experience in analytics or related area.

Skills Required: Must have experience with: Analyzing large-scale and high-dimensional datasets to discover insights (SAS, Python, Excel); Integrating and preparing large, varied datasets using SQL on cloud platform (AWS) and On Prem databases; Building and maintaining predictive machine learning and deep learning models using statistical algorithms like Regression, Decision Trees, Ensemble methods, Segmentation, Clustering (SAS, Python, R); Identifying target audience using analytic models and segmentation techniques (SAS, Python); Developing experimental design approaches to validate findings and test hypothesis; Measuring effectiveness of trial elements using A/B and Multivariate testing; Assigning attribution and measuring ROI of marketing activities; Building interactive and impactful dashboards using Tableau and Excel; Cross-functional collaboration to gain an understanding of business problems and opportunities; Managing external vendors to deliver critical advanced analytics projects; Presenting actionable insights using data and analytics to non-technical audience. Employer will accept any amount of experience with the required skills.