Principal, Data Scientist

Walmart•Bentonville, AR

3d•Onsite

About The Position

What you'll do... Position: Principal, Data Scientist Job Location: 702 SW 8th Street, Bentonville, AR 72716 Duties: Tech. Problem Formulation: 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: 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: 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: 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 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. Develop newer techniques by leveraging the latest trends in machine learning, artificial intelligence to train algorithms to apply models to new data sets.

Requirements

Minimum education and experience required: Master’s degree or the equivalent in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field plus 3 years of experience in analytics or related experience; OR Bachelor’s degree or the equivalent in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field plus 5 years of experience in analytics or related experience.
Must have experience with: Implement data science solutions on Cloud environments :Google Cloud, Azure
Coding in object-oriented language :Python, C++
Use statistical software :R
Use ML and Deep learning frameworks :Scikitlearn, TensorFlow, PyTorch, Keras
Computer Vision:ResNet50,fast.ai for image segmentation and object detection
Big Data technologies :BigQuery, Spark, Hive, Hadoop for Data Integration: Databricks, Snowflake, Collibra
Feature engineering, extraction, reduction techniques using Python, R
Exploratory data analysis with data warehouses via database querying language :SQL, setting up ETL jobs with Glue, Knime, Alteryx, Data Visualization using Python, Tableau, PowerBI, Matplotlib, Seaborn, ggplot, Spotfire for insights to stakeholders
Implement supervised and unsupervised ML techniques: K-means clustering,Principal Component Analysis,Hierarchical Clustering,ARIMA,Hierarchical linear modeling and Gradient Boosting on Python,R, operational research solutions with optimization libraries (PuLP,CPlex), optimization and neural nets,natural language processing with gensim,BERT, deploy time series forecasting solutions with ML techniques like DeepAR on Python and R
Identify model evaluation metrics and apply best techniques for model testing and tuning to assess accuracy, fit, validity, robustness using advanced statistics
Translate business problems into production grade scalable data science solutions with
MLflow,Docker,Kubernetes implementation generating actionable insights.
Employer will accept any amount of experience with the required skills.

Responsibilities

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.
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.
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.
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 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.
Develop newer techniques by leveraging the latest trends in machine learning, artificial intelligence to train algorithms to apply models to new data sets.