Data Scientist

Summary

Join Versapay as a primary Data Scientist to drive innovation and fuel growth by owning the entire data science lifecycle, from identifying high-impact opportunities to deploying and optimizing models that directly influence business strategy and bottom line.

Requirements

• Experience : 3+ years of hands-on experience in a Data Scientist role, with a demonstrated track record of success in applying data science techniques to solve a variety of business problems
• Builder' Mentality: An ability to thrive in fast-paced environments with a track record of defining and prioritizing high-impact initiatives. Experience initiating and leading data science projects from ideation to deployment
• Education: Master's degree or higher in a quantitative field such as Data Science, Statistics, Mathematics, Computer Science, or a related field
• Data-driven Innovation: Proven ability to formulate clear hypotheses, design and execute experiments to test those hypotheses, and rigorously analyze the results with a strong understanding of statistical concepts
• Empathetic Collaboration: Able to communicate both persuasively and positively - assertive with humility. Demonstrated ability to present complex concepts to non-technical audiences. Maintain high standards for verbal and written communication
• Strong proficiency in Python, especially expertise in data manipulation and analysis libraries such as Pandas, NumPy, and Scikit-learn
• Experience with machine learning algorithms and techniques, including regression, classification, clustering, and time series analysis
• Familiarity with data visualization tools, such as Tableau or Power BI
• Knowledge of SQL and experience working with relational databases, especially cloud data warehouses (e.g. Snowflake)

Responsibilities

• Data Exploration and Analysis: Identify high impact business opportunities through in-depth exploration and analysis of complex datasets to uncover insights and trends
• Model Development and Deployment: Prototype, develop, and deploy predictive and machine learning models to solve business operations challenges and support strategic decision-making
• Experimentation and Optimization: Design and execute experiments to test hypotheses and optimize model performance
• Data Visualization and Communication: Create clear and compelling visualizations to communicate insights and recommendations to stakeholders at all levels
• Collaboration: Work closely with cross-functional teams, including Data Engineering, Analysts, and business stakeholders, to understand business requirements and ensure the successful implementation of data science solutions