Lead Data Scientist
The role will be performed within the Reporting & Analytics pillar of Network Operations function, reporting directly to the Reporting & Analytics Lead.
The Reporting & Analytics pillar has strong collaboration between the business and technical teams, driving innovation, an agile mind-set and a hands-on approach to all aspects of analytic application design; business requirements, analytics delivery, implementation, deployment and transition to business as usual service.
The role is responsible for the development and maintenance of Machine Learning products and applications and advanced Data Science capabilities to support the Network Operations strategic and operational decisions. The role holder will be responsible for leading and managing the data science team.
The role holder will explore and leverage the internal and external data to deliver predictive analytics solutions that add value to the Network Operations decision-making processes through the good use of machine learning algorithms and leading edge technologies.
- Lead the development and implementation of data science solutions from beginning to end.
- Translate business needs into data science problem statements to develop predictive analytics models and applications.
- Explore both, internal and external data sources to proactively identify opportunities for innovation to deliver data-centric business insights that support business decisions
- Manage the delivery of PoC projects and work with the Reporting & Analytics Lead to formulate the business case for any new and innovative products and applications.
- Facilitate resolution of complexities and simplification of solution options by bringing together business and technical SMEs i.e. data scientists, front-end and back-end developers, and other relevant resources.
- Present insights to PwC leaders and C-suite executives on a regular basis using business acumen, as well as an ability to translate complex technical concepts into business terms and actionables.
- Manage the transition of data science projects into business as usual self-service applications.
- Work with the Reporting & Analytics Lead to formulate the product roadmap and the at scale implementation strategy.
- Manage the service demands of predictive analytics products and liaise with business stakeholders to verify insight needs and eliminate ambiguity.
- Manage stakeholder expectations by providing achievable timelines for outputs and ensuring stakeholder satisfaction.
- Actively solicit stakeholder feedback for improving the data science services.
- Propose appropriate recommendations for service, process and application improvements and optimization needs based on user/stakeholder feedback.
- Promote the use of self-served analytics and insights delivered via the Shiny apps and RStudio suite of products.
- Drive, deliver and maintain the data science standards/principles, advocate the best practice use of the self-service capabilities to ensure the consistency of delivered insights.
- Liaise with the relevant service teams in IT, Global Chief Data Office, NIS and Finance to support development activities, data quality and availability as necessary
- Assure the quality, accuracy and security of outputs prior to sign-off and distribution.
Skills and Experience:
- Experience devising, developing and deploying data science / machine learning products in Production environments.
- Experience leading and supervising on/off-shore data science teams to support simultaneous business demands whilst meeting deadlines in a fast-paced, constantly changing environment.
- Solid experience of applying data-driven mathematical/statistical/Machine Learning models using R and Python, ideally in a financial services business environment.
- Strong knowledge of statistical modelling and Machine Learning concepts, including Econometric methods for time series forecasting.
- Professional experience working with database developers and data engineers to ensure the data points are optimally tuned for machine learning algorithms and data science applications.
- Solid experience using open source technologies (Linux, PostgreSQL, R, Python) to support predictive analytics activities and outputs.
- Proven ability to solve business problems, handle conflicts, anticipate issues/concerns, troubleshoot issues, and proactively institute creative solutions quickly and in detail.
- Exposure to MLOps (Machine Learning pipelines) in production is an advantage.
- Exposure to distributed processing systems for big data workloads is an advantage (Hadoop, Spark, AWS, Google Cloud Platform, Microsoft Azure).
- Ability to establish strong working relationships with colleagues, other dependent functions and departments.
- Strong written and verbal communication, presentation, and technical writing skills.
- Familiarity with Financial Services and Management Consulting industries
Not the role for you?
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