VP - Data Science

The Data Science Head will oversee the development and implementation of data-driven solutions across the organization. The role involves leading a team of data scientists, collaborating with cross-functional teams, and delivering actionable insights to support business decisions. The ideal candidate will have a deep understanding of machine learning, statistical modeling, and advanced analytics to drive innovation and solve complex business challenges.

Key Responsibilities:

• Data Science Leadership:

Lead the data science team to develop advanced analytical models and machine learning algorithms to address business problems.

Provide technical guidance and mentorship to data scientists, fostering an environment of continuous learning and development.

Collaborate with business stakeholders to understand their challenges and objectives, translating them into data science projects

• Model Development and Deployment:

Design and implement predictive models, recommendation engines, and optimization algorithms using machine learning and statistical techniques.

Oversee the end-to-end lifecycle of models, including data collection, feature engineering, model development, validation, and deployment. Ensure that models are scalable, efficient, and deployed into production with proper monitoring and maintenance processes.

• Data Strategy and Innovation:

Define the data science strategy and roadmap aligned with the organizations business goals.

Identify opportunities to leverage data science and advanced analytics to enhance decision-making, optimize processes, and improve customer experiences.

Stay up-to-date with the latest trends and advancements in data science, machine learning, and artificial intelligence to keep the team and organization at the cutting edge.

• Collaboration with Cross-Functional Teams:

Work closely with data engineers, software developers, and business analysts to ensure data science solutions are well-integrated into the broader data ecosystem.

Collaborate with product, marketing, operations, and finance teams to develop solutions that enhance business performance.

Act as the primary point of contact for data science initiatives, presenting findings and recommendations to senior leadership and key stakeholders.

• Data Quality and Governance:

Ensure data quality and integrity across all data science initiatives by working with data engineers to implement proper data governance frameworks.

Define and enforce best practices for data science methodologies, including version control, reproducibility, and documentation of models

Work closely with IT and data management teams to ensure data infrastructure supports the teams analytical needs.

• Performance Monitoring and Reporting:

Develop dashboards and performance metrics to track the success of models and provide insights into ongoing data science projects.

Present findings and model outcomes to business stakeholders, making recommendations for business decisions based on data-driven insights. Continuously monitor model performance in production, implementing improvements as necessary

Qualifications

• Education:

Masters or Ph.D. in Data Science, Computer Science, Statistics, Mathematics, or related field.

• Experience:

8+ years of experience in data science, with at least 4 years in a leadership role.

Proven experience leading a team of data scientists, delivering end-to-end data science solutions, and driving business outcomes.

Strong background in machine learning, statistical modeling, data mining, and advanced analytics.

• Technical Skills:

Proficiency in programming languages such as Python or R for data analysis and model development.

Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).

Strong SQL skills for data manipulation and extraction from large-scale data sets.

Hands-on experience with big data technologies such as Hadoop, Spark, or cloud platforms (AWS, Azure, GCP).

Experience with data visualization tools (e.g., Tableau, Power BI, Matplotlib).

• Leadership & Communication:

Ability to translate complex technical concepts into actionable insights for non-technical stakeholders.

Excellent leadership skills with a proven ability to manage, mentor, and develop a team.

Strong presentation skills to articulate data-driven insights to senior management.