Data Scientist
- Wayne, PA
- $120,000-150,000 per year
- Permanent
- Full-time
- Develop and deploy statistical and machine learning models to solve a range of business problems, including forecasting, classification, segmentation, and causal inference.
- Collaborate across business and technology teams to scope projects, identify opportunities, and deliver actionable insights.
- Contribute to AI and automation-related initiatives, helping evaluate and implement GenAI and workflow solutions.
- Support scalable data solutions using modern cloud tools and APIs, and help maintain analytical and data science codebases.
- Bachelor’s degree in Statistics, Computer Science, Mathematics, or a related discipline.
- Master’s degree in Statistics, Computer Science, Mathematics, or a related discipline.
- Minimum 4 years of hands-on experience in data science or a related field.
- Preferred 6+ years of hands-on experience in data science or a related field.
- Strong foundation in statistical modeling and machine learning, including regression, time series forecasting, tree-based algorithms, optimization, and causal analysis. You should be comfortable applying the right methods to the right problems.
- Strong Python and SQL. Familiarity with R is a plus, especially for supporting legacy code.
- Understanding of the Microsoft Copilot ecosystem; including Copilot Studio, Azure Foundry, and prompting best practices. Experience building or configuring Copilot agents is a huge plus.
- Experience working with Snowflake or a similar cloud data platform.
- Ability to work with APIs, write modular and reproducible code, and support basic data engineering tasks (e.g., pipeline design, version control).
- Familiarity with robotic process automation tools such as UiPath, Power Automate, or similar platforms.
- Awareness of workflow automation tools (e.g., n8n, Apache Airflow, etc) and how they integrate with predictive models or LLMs.
- Understanding of how automation, GenAI, and decision systems can be orchestrated to create agentic or semi-autonomous solutions.
- Exposure to MLOps or DevOps practices such as CI/CD pipelines, model versioning, or containerization.
- General understanding of Salesforce and its role in enterprise data ecosystems.
- Strong communication skills with the ability to translate technical work into clear, business-relevant insights.
- Ability to balance hands-on technical development with broader strategic thinking.
- Curiosity about emerging technologies and a pragmatic approach to applying them in real business contexts.