VP - Data Science

The VP - Data Science 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 organization’s 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 team’s 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:

• Master’s 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 and 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.

Preferred Skills:

• Experience with deep learning techniques, NLP, computer vision, or reinforcement learning.
• Familiarity with MLOps and model deployment in production environments.
• Knowledge of cloud-based data platforms and services for large-scale machine learning solutions.
• Experience working in an agile environment, with strong project management skills.

Soft Skills:

• Strong problem-solving and analytical thinking.
• Excellent communication and collaboration skills.
• A passion for innovation and continuous improvement in data science practices.