Key element of the position include:
- Lead the development and implementation of the company group data science strategy, including the use of predictive analytics, delivering insights, and driving strategic growth.
- Build and develop the best team of data scientists and machine learning engineers.
- Define and cultivate best practices in analytics instrumentation and experimentation.
- Develop and implsment best practioes for data analysis and modeling, ensuring that we are using cutting-edge technigues to derive insighis from complex data sets.
- Collaborate with senior leaders and crossfunctionat teams within the groups in problem framing and conception of ideas to develop consensus and execute the machine learning use cases.
- Lead and build machine learning models through all phases of development from design, testing, data gathering, training, evaluation, validation, governance, and implementation.
- Maintain the heaith of machine Isarning systems, including speed, model accuracy, reliability. and performance.
- Champion effort to design and implement strategic fan growth initiatives such as models for lifetime value forecasting, subscriber lapse propensity, lead scoring, season:ticket member retention, and media mix modeling.
- Measure outcomes and translate analytical insights into recommendations that are clearty linked to the organization’s strategy and goals.
- Take an active innovation rols in the company group by identifying nsw business processes that could benefit from Data Science support.
- Master Degres in Computer Science, Statistics, Data Science, or relevant analytics field with minimum 8 years experience in data science.
- Fast learner business processes, especially the mining industry.
- Deep hands-on experience in machine leaming, relational databases, and open-source programming languages (Python, R) for large-scate data analysis.
- Deep hands-on in database sets and engines, e.g. POSTGRE, SAP HANA, ORACLE, MS SQL, and HADOOP.
- Proficient in project Management concepts such as waterfall, agila, and Lean.
- Proven track record of delivering business value through analytical models.
- Experience in developing advanced data scienog and machine learning infrastructure and systems.
- Strong knowiedge of data science technotogies and machine Isaming concepts and experience in developing models from research, to a proof of concept. to a production pipeline.
- Deep understanding of working in cloud and hybrid environments, e.g., GCP, AWS, AZURE.
- Excellent communication with prior experience coordinating multiple stakeholder groups.
- Passion for continuous improvement.
- A cultural leader who can infuse the culture of creativity and innovation.