We are seeking a motivated and curious Associate Data Scientist to join our growing data science and analytics team. This role is ideal for someone who enjoys working across the full data value chain, from understanding business needs and preparing data to building reports, dashboards, analytical models, and supporting machine learning and AI initiatives.
This is not a pure data science or machine learning role. A significant portion of the work will involve data analyst and business intelligence responsibilities, including data modeling, exploratory analysis, reporting, dashboard creation, and translating business requirements into actionable insights using tools such as Tableau and Microsoft Power BI. The role will also provide opportunities to contribute to machine learning, AI, and advanced analytics use cases under the guidance of senior team members.
In this role, you will support initiatives that drive decisions, insights, and innovation across our internal, partner, and customer ecosystems. You will explore data, build analytical solutions, support model development, and translate findings into practical recommendations that contribute to business growth and customer success.
This is a great opportunity for individuals early in their data science or analytics career to build hands-on experience in a collaborative environment, working with real-world data and solving practical business problems.
Key Responsibilities
- Business Partnership & Requirements Gathering
- Partner with cross-functional business teams to understand objectives, challenges, data needs, and success metrics across functions.
- Translate business goals into clear, well-defined requirements for analytics, reporting, dashboards, data models, and machine learning solutions.
- Work with stakeholders to clarify reporting needs, define key metrics, and ensure deliverables support effective decision-making.
- Communicate analytical findings, dashboard insights, and modeling results in a clear, concise, and actionable manner to both technical and non-technical stakeholders.
Data Analytics, Reporting & Business Intelligence
- Design, build, and maintain data models that support reporting, dashboarding, exploratory analysis, and advanced analytics use cases.
- Develop and maintain reports, dashboards, and visualizations using Tableau and Microsoft Power BI for internal, partner-facing, and business decision-support use cases.
- Work with business users to define KPIs, metrics, dimensions, filters, and reporting logic.
- Explore, cleanse, transform, and validate raw data from various sources to prepare integrated datasets for analytics, reporting, and modeling.
- Conduct exploratory data analysis to uncover patterns, anomalies, trends, and business opportunities.
- Ensure reports and dashboards are accurate, reliable, user-friendly, and aligned with stakeholder needs.
- Apply analytics best practices to improve insight quality, usability, performance, and scalability.
Machine Learning, AI & Advanced Analytics
- Support the development, validation, and deployment of machine learning models for identified business and product use cases.
- Assist with feature engineering, model evaluation, performance tracking, and documentation.
- Contribute to AI and advanced analytics initiatives where appropriate, including experimentation, prototyping, and evaluation of new machine learning models.
- Work under the guidance of senior team members to apply statistical, machine learning, and analytical techniques to practical business problems.
- Help translate model outputs into business insights, recommendations, and measurable outcomes.
Collaboration & Enablement
- Work closely with data engineering teams to ensure data pipelines, data structures, and semantic layers align with analytical, reporting, and modeling needs.
- Collaborate with product, business, and technical teams to support data-driven decision-making.
- Contribute to defining and evolving an analytics and data science roadmap aligned with business and product priorities.
- Act as a contributor and advocate for data literacy, consistent metrics, and effective use of data across the organization.
- Document data definitions, assumptions, analytical methods, reporting logic, and model outputs to support transparency and reuse.