DISTINGUISHED, DATA SCIENTIST

Position Summary...Provides overall direction by analyzing business objectives and customer needs; developing, communicating, building support for, and implementing business strategies, plans, and practices; analyzing costs and forecasts and incorporating them into business plans; determining and supporting resource requirements; evaluating operational processes; measuring outcomes to ensure desired results; identifying and capitalizing on improvement opportunities; promoting a customer environment; and demonstrating adaptability and sponsoring continuous learning. Develops and implements strategies to attract and maintain a highly skilled and engaged workforce by diagnosing capability gaps; recruiting, selecting, and developing talent; supporting mentorship, workforce development, and succession planning; and leveraging the capabilities of new and existing talent. Cultivates an environment where associates respect and adhere to company standards of integrity and ethics by integrating these values into all programs and practices; developing consequences for violations or non-compliance; and supporting the Open Door Policy. Develops and leverages internal and external partnerships and networks to maximize the achievement of business goals by sponsoring and leading key community outreach and involvement initiatives; engaging key stakeholders in the development, execution, and evaluation of appropriate business plans and initiatives; and supporting associate efforts in these areas.

What you'll do...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 evaluate proposed business cases for projects and initiatives. Translate business requirements into strategies, initiatives, and projects and aligns them to business strategy and objectives, and drives the execution of deliverables. Build and articulate the business case and return on investment and delivers work that has demonstrable value. Challenge business assumptions on topics related to one's domain expertise. Mentor the team members on new business insights and allied developments. Proactively engage in the external community to build Walmart's brand and learn more about industry practices. 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 define data requirements for various model types. Direct usage of the appropriate internal and external data sources based on business and model requirements (for example, types of databases, recommended data structures). Define data quality guidelines and align data governance. 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 explore and create automated feature generation framework. Develop standard EDA process. Develop best practices on experimentation. Drive exploratory work in newer areas of Math, Statistics, Machine Learning, and Optimization Techniques. Continuously improve the business's data analysis models. Create industry-leading performance by leveragi Model Assessment and Validation: Requires knowledge of model fit testing, tuning, and validation techniques (e.g., Chi square, ROC curve, root mean square error etc.); Impact of variables and features on model performance To identify and review model evaluation metrics based on analytical requirements. Apply suitable techniques for model testing and tuning, to assess accuracy, fit, validity, and robustness. Ensure testing information is documented and maintained by the team. Model Deployment and Scaling: Requires knowledge of impact of variables and features on model performance; understanding of servers, model formats to store models. To deploy models or model ensemble and ensure sustainability and maintenance overtime. Implement model monitoring and model life-cycle management practices. Assist in creation of innovative user interfaces and support the use of models through collaboration with key stakeholders. Code Development and Testing: Requires knowledge of coding languages like SQL, Java, C++, Python and others; Testing methods such as static, dynamic, software composition analysis, manual penetration testing and others; Business, domain understanding. To review the solution and application design to ensure it meets business, technical, and data requirements. Identify language and libraries to use in the development process. Map test cases to business and functional requirements. Create proof of concepts. Review and troubleshoot code in line with final designs. Identify and recommend the appropriate testing methodology. Identify the environment(s) for deployment. Identify and recommends modifications of application based on different environment requirements. Identify modifications needed for scalability and drives the change. Monitor applications in production and leads development of patches where required. Review and ensure all code documentation is complete and updated periodically. Data Visualization: Requires knowledge of Visualization guidelines and best practices for complex data types; Multiple data visualization tools (for example, Python, R libraries, GGplot, Matplotlib, Ploty, Tableau, PowerBI etc.); Advanced visualization techniques/ tools; Multiple story plots and structures (OABCDE); Communication & influencing technique; Emotional intelligence. To identify and recommend the most suitable visualization tools based on context. Generate appropriate graphical representations of data and model outcomes. Understand customer requirements to design appropriate data representation for complex data sets and drive User Experience designers and User Interface engineers to build front end applications. Define application design based on customer requirements. Build compelling stories based on context to integrate multiple pieces of information into cohesive insights. Present to and influence diverse audiences using the appropriate data visualization frameworks and Data Strategy: Requires knowledge of understanding of business value and relevance of data and data enabled insights / decisions; Appropriate application and understanding of data ecosystem including Data Management, Data Quality Standards and Data Governance, Accessibility, Storage and Scalability, etc.; Understanding of the methods and applications that unlock the monetary value of data assets. To understand, articulate, interpret, and apply the principles of the defined strategy to unique, moderately complex business problems that may span one or more functions or domains.

Minimum Qualifications...

Outlined below are the required minimum qualifications for this position. If none are listed, there are no minimum qualifications.

Minimum Qualifications:Option 1: Bachelor's degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 6 years' experience in an analytics related field. Option 2: Master's degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 4 years' experience in an analytics related field. Option 3: 8 years' experience in an analytics or related field.

Preferred Qualifications...

Outlined below are the optional preferred qualifications for this position. If none are listed, there are no preferred qualifications.

Primary Location...4,5,6, 7 Floor, Building 10, Sez, Cessna Business Park, Kadubeesanahalli Village, Varthur Hobli , India