**Responsibilities**
- Lead data analysis projects from inception to completion, including data collection, cleansing, processing, and analysis.
- Develop and refine pricing models and algorithms to optimize pricing decisions.
- Develop predictive models and algorithms to support business forecasting, optimization, and decision-making processes.
- Collaborate closely with cross-functional teams, including Operations, Strategy, Business, Product and Engineering, to understand the business requirements and translate them into actionable insights.
- Design and develop data visualizations, dashboards, and reports to communicate findings and recommendations effectively to stakeholders.
- Stay current with industry trends, emerging technologies, and best practices in data analysis and data science, and contribute to continuous improvement initiatives within the team.
**Requirements**:
- Minimum 4 years of experience with data analysis, business intelligence, or data science roles, preferably a fast-paced environment in a FinTech firm.
- Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Economics, or related field.
- Previous experience in working along with the Pricing teams is preferred, in the automotive industry.
- Understanding of the used car market, pricing dynamics, and customer behavior in the automotive industry is a plus.
- Proficiency in SQL, Github, and Python, or other programming languages for data analysis and statistical modeling.
- Experience with data visualization tools such as Tableau, Power BI, or matplotlib or seaborn is good to have.
- Strong analytical and problem-solving skills, with the ability to think critically and independently.
- Excellent communication and interpersonal skills, with the ability to present technical concepts to non-technical stakeholders.
- Proven track record of delivering high-quality analysis and actionable insights that drive business impact.
- Experience working with large datasets, structured and unstructured data, and data preprocessing techniques.
- Familiarity with basic machine learning techniques such as regression, classification, clustering, and time series analysis.