Senior Director, Data Science

Position: (USA) Senior Director, Data Science
Position Summary...

What you'll do...

Essential Functions

An individual must be able to successfully perform the essential functions of this position with or without a reasonable accommodation.

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 collaborate with multiple business stakeholders to gain an understanding of

complex organizational problems from varied perspectives to create effective technology focused solutions. Influence the business to redefine the

problem statement. Design multi-stage, data-driven solutions to real-world problems for issues that are amenable to a data-driven solution. Redefine

data analytics, big data analytics, automation goals, and deliverables by leveraging experience with the business problem. Identify return on

investment.

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.

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

leveraging new and creative data-sources. Employ the latest in machine learning in the department, Scope, design, and implement new machine learning models to support the business's initiatives and programs with a view of achieving overall objectives and targets. Guide teams in the

development and delivery of big data predictive technologies models, and fully working prototypes of complex algorithms using readily available

libraries. 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 write code to develop the

required solution and application features by determining the appropriate programming language and leveraging business, technical, and data

requirements. Create test cases to review and validate the proposed solution design. Create proofs of concept. Test the code using the appropriate

testing approach. Deploy software to production servers. Contribute code documentation, maintain playbooks, and provide timely progress updates.

Data Visualization:
Requires knowledge of Visualization guidelines and best…