Sr Data Scientist

As the Data Scientist at UniAthena, your role is to own the end-to-end data intelligence layer, transforming raw data into actionable insights that drive real business impact. You will be responsible for aggressively driving predictive modeling, identifying growth opportunities, and directly influencing revenue, learner engagement, and product decisions through data-driven strategies. Key Responsibilities: - Build and deploy high-impact machine learning models for lead scoring, enrollment prediction, retention, and learner behavior. - Take complete ownership of data analysis across marketing, product, and academic funnels, ensuring proactive problem-solving rather than passive reporting. - Translate ambiguous business problems into structured analytical and ML solutions. - Identify revenue leakage, drop-offs, and inefficiencies, proactively solving them using data-driven approaches. - Design and implement end-to-end data pipelines for model training, validation, and deployment. - Continuously enhance model accuracy, performance, and scalability in production environments. - Drive experimentation such as A/B testing and cohort analysis to optimize conversion rates and learner engagement. - Collaborate closely with cross-functional teams to ensure data actively informs decision-making processes. - Develop dashboards and analytical frameworks directly linked to business KPIs. - Challenge existing assumptions and promote a data-backed decision culture across teams. Qualifications Required: - Robust expertise in Python (Pandas, NumPy, Scikit-learn) and a solid understanding of ML algorithms. - Proven experience in building and deploying predictive models in real-world scenarios. - Proficiency in SQL and data manipulation for large datasets. - Familiarity with data visualization tools like Power BI and Tableau for generating business-facing insights. - Hands-on experience with model deployment and cloud environments, preferably AWS or Azure. - Strong business acumen with the ability to connect models to revenue, conversion, and retention metrics. As the Data Scientist at UniAthena, your role is to own the end-to-end data intelligence layer, transforming raw data into actionable insights that drive real business impact. You will be responsible for aggressively driving predictive modeling, identifying growth opportunities, and directly influencing revenue, learner engagement, and product decisions through data-driven strategies. Key Responsibilities: - Build and deploy high-impact machine learning models for lead scoring, enrollment prediction, retention, and learner behavior. - Take complete ownership of data analysis across marketing, product, and academic funnels, ensuring proactive problem-solving rather than passive reporting. - Translate ambiguous business problems into structured analytical and ML solutions. - Identify revenue leakage, drop-offs, and inefficiencies, proactively solving them using data-driven approaches. - Design and implement end-to-end data pipelines for model training, validation, and deployment. - Continuously enhance model accuracy, performance, and scalability in production environments. - Drive experimentation such as A/B testing and cohort analysis to optimize conversion rates and learner engagement. - Collaborate closely with cross-functional teams to ensure data actively informs decision-making processes. - Develop dashboards and analytical frameworks directly linked to business KPIs. - Challenge existing assumptions and promote a data-backed decision culture across teams. Qualifications Required: - Robust expertise in Python (Pandas, NumPy, Scikit-learn) and a solid understanding of ML algorithms. - Proven experience in building and deploying predictive models in real-world scenarios. - Proficiency in SQL and data manipulation for large datasets. - Familiarity with data visualization tools like Power BI and Tableau for generating business-facing insights. - Hands-on experience with model deployment and cloud environments, preferably AWS or Azure. - Strong business acumen with the ability to connect models to revenue, conversion, and retention metrics.