Applied Data Scientist & Market Research Analyst Internship
Plum Boro
Internship
Student (College)
Position Summary
The Applied Data Scientist and Market Research Analyst will lead market research, analytics, and insight generation to support the commercialization and evolution of our product flagship platform. This role blends market intelligence, customer research, data visualization, and applied data science, with a focus on identifying market opportunities, validating customer demand, and developing analytical models that learn from legacy historical data.
The role will identify and recommend target markets, audiences, contacts, and pricing strategies; establish customer interest through outreach and surveys; analyze competitor offerings; and develop analytical algorithms and predictive models using historical healthcare data.
Key Responsibilities
Market Research & Segmentation
(Insert standard organizational EEO language here.)
The Applied Data Scientist and Market Research Analyst will lead market research, analytics, and insight generation to support the commercialization and evolution of our product flagship platform. This role blends market intelligence, customer research, data visualization, and applied data science, with a focus on identifying market opportunities, validating customer demand, and developing analytical models that learn from legacy historical data.
The role will identify and recommend target markets, audiences, contacts, and pricing strategies; establish customer interest through outreach and surveys; analyze competitor offerings; and develop analytical algorithms and predictive models using historical healthcare data.
Key Responsibilities
Market Research & Segmentation
- Conduct market research and analysis to identify and define market niches, use cases, and target audiences.
- Develop customer and buyer personas, including decision‑makers, influencers, and end users.
- Identify market trends and adoption barriers across relevant industries.
- Identify and recommend appropriate markets, audiences, target contacts, and pricing models.
- Support development of packaging and commercialization strategies informed by market and competitive insights.
- Assist with market sizing and opportunity assessment activities.
- Establish and validate customer interest in core product through emails, surveys, interviews, webinars, and outreach programs.
- Analyze and synthesize customer feedback to inform product positioning, messaging, and roadmap considerations.
- Collaborate with internal stakeholders to translate customer insights into actionable recommendations.
- Research competitor products and solutions, identifying strengths, weaknesses, differentiators, and market positioning.
- Maintain competitive intelligence to support product marketing, pricing discussions, and sales enablement.
- Create data visualizations using third‑party tools (e.g., R, Jupyter Notebook) by leveraging and analyzing data.
- Use visual analytics to support market analysis, customer insights, competitive comparisons, and product storytelling.
- Develop and train analytical algorithms and predictive models to predict insurance reimbursement amounts using legacy historical healthcare data.
- Apply exploratory data analysis, statistical techniques, and feature engineering to large, heterogeneous datasets.
- Translate analytical and model outputs into clear, actionable insights for technical and non‑technical stakeholders.
- Expand research efforts to identify customers and use cases that would benefit from a chart mining extraction capability.
- Translate chart mining and data extraction value into market‑ready positioning aligned with other product capabilities.
- Bachelor’s degree in Marketing, Business, Data Analytics, Data Science, or a related field (or equivalent experience).
- Experience conducting market research, data analysis, or product/market analytics.
- Strong analytical and problem‑solving skills.
- Ability to clearly communicate insights through written, visual, and verbal formats.
- Experience working with analytical tools such as R, Python, and Jupyter Notebook.
- Familiarity with machine learning, predictive modeling, or statistical analysis.
- Experience working with healthcare, insurance, claims, or reimbursement data.
- Experience supporting software or data platform products.
- Ability to work cross‑functionally with product, engineering, and business teams.
- Hybrid or remote‑friendly
(Insert standard organizational EEO language here.)
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