Job Description:
Utilizing AI and statistical techniques to extract insights from complex datasets and drive data-driven decision-making. Join S.T.A.R.S Pvt Ltd to transform raw data into actionable business intelligence and develop predictive models that solve real-world challenges. You will work with diverse datasets to uncover patterns, build machine learning models, and communicate insights to stakeholders.
Collaborate with product managers, engineers, and business teams to identify opportunities for data-driven solutions. This role combines statistical analysis, machine learning expertise, and business acumen to deliver insights that directly impact client success and drive strategic decision-making across multiple industries.
Responsibilities:
- Analyze complex datasets to identify trends, patterns, and actionable insights for business optimization
- Design and build predictive models using machine learning algorithms for forecasting and classification tasks
- Create compelling data visualizations and dashboards to communicate findings to technical and non-technical stakeholders
- Collaborate with engineering teams to implement data pipelines and automated reporting systems
- Conduct A/B testing and statistical experiments to measure impact of product features and business initiatives
- Work with clients to understand their data challenges and develop customized analytics solutions
Preferred Qualifications:
- Master's degree in Data Science, Statistics, Mathematics, Computer Science, or related quantitative field
- 2+ years experience in data science, analytics, or machine learning with proven track record of insights delivery
- Proficiency in Python or R, SQL, and data visualization tools like Tableau, Power BI, or similar platforms
- Strong statistical knowledge including hypothesis testing, regression analysis, and experimental design
- Experience with machine learning libraries (scikit-learn, pandas, numpy) and cloud analytics platforms
- Excellent communication skills for presenting complex analytical findings to diverse business audiences
