Senior Data Scientist
P
Point Digital Finance, Inc.Home equity
Location: United States
Local or 100% Remote
Work from anywhere in the U.S.Full-TimeSenior
Salary156,750 - 198,450 USD per year
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Job Details
- Experience
- 4–5 years of hands-on experience in data science, applied statistics, or machine learning (internships and projects count).
- Required Skills
- PythonSQLMachine LearningMicrosoft Power BINumpyPyTorchTableauData sciencePandasTensorflowMicrosoft ExcelData visualizationscikit-learn
Requirements
- 4–5 years of hands-on experience in data science, applied statistics, or machine learning (internships and projects count).
- Familiarity and experience working with AI toolkits such as Claude or Gemini or similar.
- Strong foundation in Python (Pandas, NumPy, scikit-learn; PyTorch or TensorFlow a plus).
- Familiarity with SQL for data extraction and transformation.
- Experience with basic machine learning workflows: preprocessing, feature engineering, model training, validation, and monitoring.
- Understanding of data visualization tools (e.g., Tableau, Power BI, or Python libraries like Matplotlib/Seaborn).
- Curiosity and eagerness to learn — especially around model monitoring, forecasting, and marketing analytics.
- Excellent problem-solving skills and ability to communicate insights clearly.
- Bachelor’s degree in Computer Science, Statistics, Mathematics, Economics, or a related field. A Master’s degree is a plus.
- Advanced knowledge of AI tools
- Expertise on BI dashboarding tools such as Sigma or Tableau preferred
- Strong Excel skills
Responsibilities
- Design, train, and deploy machine learning models to solve marketing, forecasting, and operational problems.
- Track model performance, detect drift or deterioration, and assist in implementing corrective measures.
- Develop time-series forecasts and performance dashboards that inform marketing strategies and broader business decisions.
- Clean, transform, and maintain structured datasets to ensure reliable inputs for analytics and modeling.
- Partner with marketing, product, and business teams to translate requirements into data-driven solutions.
- Produce clear documentation of models, data pipelines, and processes; communicate insights to technical and non-technical stakeholders.
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