Lowes - Senior Data Scientist

Purpose of Role:

- The principle purpose of the Principal Data Scientist is to provide advanced analytical capabilities to support data science initiatives. These include insightful business, website, and statistical analyses that yield tangible results for Lowe's and Lowe's Digital. Current areas of interest and specialization on the team include: predictive modeling; personalization and recommendation algorithms; natural language processing and text mining; search recall, precision, ranking, and related problems; optimization and mathematical programming with applications to labor scheduling, inventory and capacity planning, network flows, and supply chain optimization.

- In addition, the Principal Data Scientist is responsible for taking the lead on analytic and automation projects, developing expertise in problem analysis, solution implementation, and ongoing optimization. The Principal Data Scientist is expected to stay current on industry trends, emerging technologies, new methodologies, and provide guidance to team members.

- To accomplish this, the Principal Data Scientist must have a strong interest in data science, statistical analyses, customer behavior, and the retail space, while demonstrating comfort in dealing with ambiguity. Also, the Principal Data Scientist must possess a high level of intellectual curiosity in proactively identifying opportunities for improved results.

- This position should expect to collaborate successfully across business functions within Lowe's and Lowe's Digital.

Outputs & Key Responsibilities

Measure and Evaluate

- Identify innovative ways for measuring and improving customer engagement

Business Modeling

- Conduct deep statistical analyses, including predictive and prescriptive modeling

- Develop machine learning models and data science frameworks to support key business initiatives such as personalization & recommendations; search recall, relevancy & ranking; supply chain & network optimization; etc.

Business Influence

- Keeps pace with change and maintains a competitive advantage within the marketplace by seeking out and learning changes in the field

People and Self Leadership

- Responsible for working across the Data Science team, other analytics colleagues, and business partners (such as Personalization, Search & Navigation, SEO & Promotions, Social, Inventory Planning, Supply Chain, Services, etc.) to accomplish tasks

- Takes lead on project discovery, project coordination, and project implementation

- Engage in developing the next generation Principal Data Scientist through the Senior Data Scientists

- Develops communications, conveying message at the appropriate level for the given audience

Core and Functional/Technical Competencies

Core Competencies :

- Communicating Effectively- The skill to write, present, and listen effectively. This includes adjusting the amount, style, and content of the information to fit the audience. This also includes the ability to convey a compelling message.

- Decision Making- The ability to solve tough and complex problems. This includes the ability to effectively glean meaning from available data, be a quick study of the new and different, and personal wisdom and experience to arrive at the best solution given the information available, and use multiple problem-solving tools and techniques.

- Demonstrating Personal Flexibility- The ability to demonstrate resourcefulness and resilience in the face of change, obstacles, and adversity. This includes adapting to competing demands and shifting priorities. This also includes improving adaptability, pursuing new skills and knowledge, and regularly seeking feedback from others.

- Dealing with Trouble- The ability to quickly and directly address problems, find common ground and persevere on tough assignments. This includes a willingness to be centrally involved in debates and facilitating conflict discussion and resolution.

- Focusing on the Bottom Line- The ability to attack work activities with drive and energy, understanding the impact of work on key metrics and making decisions that are in the company's best interest. This includes not being afraid to initiate action before all the facts are known and driving value-added work tasks to completion.

- Getting Work Done Through Others - The ability to select and manage the best talent for a job or role. This includes the ability to get the most and best out of people by leveraging their strengths, setting and communicating goals, measuring accomplishments, holding people accountable, and giving useful feedback. This also includes delegating and developing others, keeping people informed, and providing coaching for today and for the future.

Functional/Technical Competencies

1. Big Data Management and Analytics (Advanced)

Knowledge and ability to plan and execute big data management and analytics. Carries out complex tasks associated with big data analytics. Uses a variety of software tools to perform advanced analytics with big data. Integrates big data technology such as NoSQL databases, Hadoop, and MapReduce. Extends database security into big data environments; automates compliance and data security management. Through big data analytics, identifies correlations that reveal trends and determine conditions. Helps define the parameters of big data for the organization; tests different data management options.

2. Predictive Analytics (Thought Leader)

Knowledge of, experience with and ability to use the tools, processes, and practices for predicting business trends and providing forecasts that drive business decisions and business planning. Demonstrates in-depth knowledge of implementing predictive analytics for multiple functions and geographies. Summarizes and effectively communicates conclusions from complex models to a wide range of audiences. Champions the use of predictive modeling; is an acknowledged subject-matter expert. Discusses case studies of improper or erroneous predictions, associated causes and lessons learned. Compares and contrasts various statistical concepts and their use in predicting different business situations. Monitors industry trends and marketplace developments relevant to predictive analytics.

3. Statistical Analysis and Measurement (Advanced)

The ability to guide others on a variety of statistical measurements, tools, and techniques. This includes the ability to use a variety of statistical concepts to analyze different business situations, provide management reports that identify potential business performance problems and propose the benefits and drawbacks of alternative sampling techniques. This also includes the ability to evaluate financial performance benchmarks for a variety of business functions and teach others about distribution theory, estimation theory, and rank test theory.

4. Analytical Thinking (Thought Leader)

The ability to orchestrate the use of business analytics for strategic decision making and to employ statistical and quantitative analysis, explanatory and predictive modeling techniques. This includes the ability to help others discover the diverse features, parameters, or considerations to take into account, implement operating metrics and performance gauges, and use business intelligence applications for tactical and strategic decisions. This also includes the ability to monitor industry trends and directions and discuss possible impact on internal strategies.

5. Data Mining (Thought Leader)

Knowledge of tools, techniques and practices in data mining technologies used to acquire essential business information. Provides expertise on the development and implementation of complex data mining applications. Guides others on tools and techniques used to apply data mining processes to specific business needs. Evaluates data mining tools and acquires those that benefit the business. Creates and justifies the business case for applying data mining processes to CRM or ERP functions. Reviews data mining applications frequently to ensure that false conclusions are not produced. Makes recommendations for business process changes based on data mining results.

6. Application Development (Intermediate)

Ability to build applications around the developed algorithms using Java, C, Scala, Python, etc. Strong theoretical understanding of machine learning algorithms and the ability to tweak them to suit Lowes- needs

Qualifications

Minimum Qualifications :

- 4 - 8 years analytical experience with a Bachelor's degree in related quantitative analytic field OR 2 - 6 years analytical experience with a Master's degree in related quantitative analytic field OR 1 - 4 years analytical experience with a PhD degree in related quantitative analytic field (Computer Science, Statistics, Physics, Economics, Industrial Engineering, Operations Research, etc.)

- Experience using multiple data systems and sources including an enterprise grade analytics tool (Hadoop, Spark, Aster, Teradata, Netezza, Redshift, Vertica, Alteryx, Tableau, etc.)

- 3+ years Programming experience (Java, Python, Scala, Rust, etc.)

- Advanced knowledge of SQL with at least 3 years of functional experience

- Knowledge of various statistical modeling or machine learning techniques with 3+ years of functional experience

- Strong business acumen and ability to understand business problems, develop hypotheses, test and learn

- Ability to form relationships/partnerships and lead by influence rather than by authority in a high matrix organization

- Ability to manage time and priorities while still driving multiple projects at once

- Strong communication skills and ability to interact with stakeholders and business partner across the organization, and at various levels (technical to creative; individual contributors to executive)

Preferred Qualifications :

- 3+ years functional experience in performing predictive analytics at a large scale enterprise

- Grasp of theoretical foundations of machine learning algorithms

- Ph.D. in related quantitative analytic field