Head of Data Sciences

Moladin is seeking a Head of Data Sciences to join the team. The Head of Data Sciences will oversee Moladin's data strategy, data-driven decision making, and analytics initiatives across the organization. At Moladin, the Head of Data Sciences will have the opportunity to work with a team of highly motivated professionals in a rapidly growing environment.

**Responsibilities**:

- Team Leadership:

- Manage and provide strategic direction to the Pricing Team, Data Science Team, and Credit Scoring Team.
- Foster a collaborative and innovative work environment, encouraging knowledge sharing and professional growth.
- Set clear goals, objectives, and key results (OKRs) for each team and ensure alignment with company objectives.
- Provide guidance and mentorship to team members, supporting their career development and growth.
- Data Science Strategy and Roadmap:

- Develop and execute a comprehensive data science strategy aligned with the company's vision and goals.
- Identify opportunities to leverage data science techniques to improve pricing models, credit scoring algorithms, and overall business performance.
- Collaborate with cross-functional teams, including Product, Engineering, and Operations, to define and prioritize data science projects and initiatives.
- Stay up-to-date with the latest advancements in data science and proactively identify new technologies and methodologies applicable to the used car industry.
- Pricing Optimization:

- Lead the Pricing Team to develop dynamic pricing models and strategies for used car.
- Collaborate with stakeholders to understand market trends, competitive landscape, and customer behaviour to drive pricing decisions.
- Conduct pricing analysis and simulations to optimize revenue, profit margins, and inventory turnover.
- Monitor pricing performance metrics and provide actionable insights to stakeholders.
- Credit Scoring and Risk Assessment:

- Oversee the Credit Scoring Team in developing and refining credit risk models and scoring methodologies.
- Collaborate with finance and operations teams to enhance credit risk assessment processes.
- Monitor and evaluate credit scoring performance, identifying opportunities for improvement and risk mitigation.
- Innovation in Used Car Technology:

- Lead the exploration and implementation of innovative technologies and use cases in the used car industry. For example, Recommendation engines (Classical, graph based, tree based etc), Fraud Detection detection framework, Cross-selling identification and Propensity Modeling among others
- Identify emerging trends, technologies, and best practices to enhance the customer experience and operational efficiency.
- Collaborate with cross-functional teams to develop and prototype new features, services, or platforms.
- Stay abreast of advancements in areas such as artificial intelligence, computer vision, natural language processing, and Internet of Things (IoT).
- Foster a culture of innovation within the Data Science team, encouraging experimentation and creative problem-solving.
- Evaluate and implement new tools, frameworks, and methodologies to improve the efficiency and effectiveness of data science processes.

**Requirements**:

- Master's or Ph.D. degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, or related disciplines).
- Proven experience (10+ years) leading and managing data science teams.
- Understanding of used car automotive industry would be a plus.
- Strong background in machine learning, statistical modelling, and data analysis.
- Proficiency in programming languages such as Python and R, along with experience working with SQL databases.
- Excellent understanding of pricing optimization, credit scoring, and risk assessment methodologies.
- Experience in developing and deploying machine learning models in a production environment.
- Strong leadership skills with the ability to inspire and motivate a diverse team.
- Excellent communication and presentation skills, with the ability to convey complex technical concepts to both technical and non-technical stakeholders.
- Ability to drive data science strategies and initiatives aligned with business goals.