Data Scientist

Job Summary:

The Senior Data Scientist is responsible for supporting data science initiatives that drive business profitability, increased efficiencies, improve automation using AI and improved customer experience.
This role applies industry-leading Agentic AI capabilities for creatively solve business problems in the Contact Center space. Data Scientists are also responsible for ensuring that developed codes are documented into a library of reusable algorithms.
Based on the specific data science team, this role would need to be knowledgeable in one or more data science specializations, such as NLP, Conversational AI, Information Retrieval, Generative AI or search.
As a Senior Data Scientist, you will also be responsible for applying advanced analytics methods and algorithms for identifying trends and providing business solutions.
This role is expected to present insights and recommendations to non-technical audiences and explain the benefits and impacts of the recommended solutions.
In addition, Data Scientists collaborate with business partners and cross-functional teams, requiring effective communication skills, building relationships, and focus on understanding the overall business area being supported.
 

Responsibilities:

35% Solution Development –
Proficiently design and develop algorithms and models to use against large datasets to create business insights.
Execute tasks with high levels of efficiency and quality.
Make appropriate selection, utilization and interpretation of advanced analytical methodologies.
Effectively communicate insights and recommendations to both technical and non-technical leaders and business customers/partners.
Prepare reports, updates and/or presentations related to progress made on a project or solution.
Clearly communicate impacts of recommendations to drive alignment and appropriate implementation.
30% Project Management & Team Support:
Work with project teams and business partners to determine project goals.
Provide direction on prioritization of work and ensure quality of work.
Provide mentoring and coaching to more junior roles to support their technical competencies.
Collaborate with managers and team in the distribution of workload and resources.
Support recruiting and hiring efforts for the team
20% Business Collaboration:
Leverage extensive business knowledge into solution approach.
Effectively develop trust and collaboration with internal customers and cross-functional teams.
Provide general education on advanced analytics to technical and non-technical business partners.
Deep understanding of IT needs for the team to be successful in tackling business problems.
Actively seek out new business opportunities to leverage data science as a competitive advantage.
15% Technical Exploration & Development:
Seek further knowledge on key developments within data science, technical skill sets, and additional data sources.
Participate in the continuous improvement of data science and analytics by developing replicable solutions (for example, codified data products, project documentation, process flowcharts) to ensure solutions are leveraged for future projects.
Define best practices and develop clear vision for data analysis and model productionalization.
Contribute to library of reusable algorithms for future use, ensuring developed codes are documented
Direct Manager/Direct Reports:

This position reports to manager or above.
This position has 0 Direct Reports.
Travel Requirements:

Typically requires overnight travel less than 10% of the time.
Physical Requirements:
Most of the time is spent sitting in a comfortable position and there is frequent opportunity to move about. On rare occasions there may be a need to move or lift light articles.
Working Conditions:
Located in a comfortable indoor area. Any unpleasant conditions would be infrequent and not objectionable.

Preferred Qualifications:
AI Orchestration: 4+ years of experience in Data Science with a focus on Conversational AI, GenAI, agentic workflows, custom tool-calling, and reasoning traces (e.g., LangSmith).
Advanced Retrieval: Mastery of RAG and vector database architectures, specifically for extracting technical specs from unstructured enterprise data.
Multimodal AI: Experience building pipelines to extract structured SKU-level intent from unstructured multimodal inputs, such as photos or handwritten lists.
Algorithmic Logic: Background in similarity scoring and attribute-matching to resolve vague technical queries.
State & Engineering: Experience building stateful AI applications that maintain context across devices and sessions.
Domain Expertise: Prior experience in B2B e-commerce, supply chain, or trade-related data is a significant plus.

Technical Expertise:

Experience in a modern scripting language (preferably Python); proficient running queries against data (preferably with Google BigQuery or SQL).
proficient utilizing statistical techniques to identify key insights that help solve business problems.
knowledgeable in Prescriptive Modeling like optimization, computer vision, recommendation, search or NLP.
working knowledge of Microsoft Excel and Power Point
Good to have:
The knowledge, skills and abilities typically acquired through the completion of a bachelor's degree program or equivalent degree in a field of study related to the job. Attracts Top Talent: Attracting and selecting the best talent to meet current and future business needs
Business Insight: Applying knowledge of the business and the marketplace to advance the organization's goals
Collaborates: Building partnerships and working collaboratively with others to meet shared objectives
Communicates Effectively: Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences
Cultivates Innovation: Creating new and better ways for the organization to be successful
Customer Focus: Building strong customer relationships and delivering customer-centric solutions
Develops Talent: Developing people to meet both their career goals and the organization's goals
Directs Work: Provides direction, delegating and removing obstacles to get work done
Drives Results: Consistently achieving results, even under tough circumstances
Nimble Learning: Actively learning through experimentation when tackling new problems, using both successes and failures as learning fodder
Optimizes Work Processes: Knowing the most efficient and effective processes to get things done, with a focus on continuous improvement
Self-Development: Actively seeking new ways to grow and be challenged using both formal and informal development channels