A chance to take the lead on a large-scale and exciting project!

We're look for a Lead Data Scientist to spearhead the design, development, implementation and maintenance and improvement of advanced data science initiatives across business units, directly aligning with strategic objectives. This role encompasses transforming innovative ideas into real-world solutions through the application of sophisticated analytical techniques such as machine learning, optimization, and cluster analysis.

The incumbent will lead and help to develop a newly formed data-scientist team in delivering impactful analytical solutions, ensuring these innovations are seamlessly embedded into business operations to drive decision-making, enhance operational efficiency, and foster a culture of continuous improvement and innovation. As part of this role the applicant will play a significant part in setting the AI & ML agenda for the company, including working with business units to define potential opportunities, and defining standards and best practice for AI & ML.

Day-to-Day Looks Like:

• Translates business needs into analytics/reporting requirements to support data-driven decisions with required information & explain ability.
• Keep abreast of the latest data science techniques and technologies. Explore and implement innovative solutions to improve data analysis, modeling capabilities, and business outcomes.
• Communicate complex data insights in a clear and effective manner to stakeholders across the organization, including non-technical audiences. Advocate for the importance and value of data-driven decision making.
• Manage use case design and build teams on day-to-day basis, providing guidance and feedback as they develop and operationalize data science models and algorithms to solve complex business problems.
• Ensure analytical insights and products are embedded into business processes.
• Ensure use case models/analytics are supported, maintained, and improved (as needed) post-development and launch.
• Guide and sign off on analytics/modelling approach, model deployment requirements, and quality assurance standards with input from use case teams and business leadership.
• Provide input to the long/term plan for the Data Science team, including key focus areas, talent acquisition, input to technology platforms, and interaction model with the rest of the organization.
• Foster a culture of innovation and continuous improvement and lead the exploration and adoption of new data science technologies and methodologies to contribute to the advancement of analytics' expertise.
• Work with wide landscape of business and technical stakeholders to proactively identify applicable new technologies and opportunities and detail and communicate how they can deliver measurable business value.
• Own the analytics solution portfolio, including model maintenance and improvements over time.

Lead Responsibilities Include:

• Directly supervises one or more employees. Carries out responsibilities in accordance with the organization's policies and applicable laws.
• Demonstrated ability to lead and manage data science projects, including, managing workflow and priorities, to ensure timely delivery of projects with high-quality outcomes.
• Proven track record of recruiting, training, and retaining a skilled data science team, identifying talent gaps, and addressing them.

Must Haves:

• A master s degree or PhD in Computer Science, Statistics, Applied Mathematics, or a related field, with at least 5 7 years experience in data science or a similar role.
• Proficient in at least one analytical programming language relevant for data science. Python ecosystem preferred, R will be acceptable, machine learning libraries & frameworks (e.g. TensorFlow, PyTorch, scikit-learn) and familiar with data processing and visualization tools (e.g., SQL, Tableau, Power BI).
• Expertise in advanced analytical techniques (e.g., descriptive statistics, machine learning, optimization, pattern recognition, cluster analysis, etc.)
• Experience with cloud computing environments (AWS, Azure, or Google Cloud Platform) and Data/ML platforms (Databricks, Spark).
• Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, monitoring, and feedback loop.
• Experience in Supervised and Unsupervised Machine Learning including classification, forecasting, anomaly detection, pattern recognition using variety of techniques such as decision trees, regressions, ensemble methods and boosting algorithms.,
• Good understanding of programming best practices, building for re-use and highly automated CI/CD pipelines.

Additional Skills:

• Proven track record of leading cross-functional teams to successfully deliver complex data-driven projects.
• Excellent problem-solving and analytical skills, with the ability to translate complex technical details into understandable business insights.
• Sees overall 'picture' and alternative approaches and develop vision of what may be possible.
• Strong interpersonal and communication skills, capable of working effectively with and developing trust from both non-technical and technical counterparts to influence key use case & enterprise decisions.

Nice to Have:

Relevant certifications such as Microsoft Certified: Azure Data Scientist Associate or AWS Certified Machine Learning are advantageous.