Senior Data Scientist will be part of a cross-disciplinary team, working closely with other data scientists, software engineers, data engineers, data managers, product owners and Portfolio managers.
- Build scalable, re-usable, impactful data science products, usually containing statistical or machine learning algorithms, in collaboration with data engineers and software engineers.
- Carry out data analyses to yield actionable business insights.
- Act as the accountable person for the statistical methods used to enquire data sets, design of Machine Learning models & defining the end-to-end data lifecycle of a data science project from ideation to production
- Translate problem statement to data architecture
- Break down data architecture requirements to tasks for other team members
- Appraise current design patterns (ex: AWS ) against requirements and adapt to the technology
- Seek / promote automated pipelines / jobs
- Communicate complex ideas in a digestible way to the business
- Adhere to and advocate for data science best practices (e.g. technical design, technical design review, unit testing, monitoring & alerting, checking in code, code review, documentation).
- Present results to peers and senior management.
- Coach, mentor and support the data science squad on the full range of end-to-end data science and solutions development activities
- Bachelor or master’s degree in computer science, Engineering, Informatics, Information Systems or in another quantitative fields
- Specialization in Machine Learning or Data Science will be an advantage
- 8 to 12 years with minimum of 5 to 7 years relevant experience
- Deep applied knowledge of data science tools and approaches across all data lifecycle stages.
- Detailed understanding of underlying mathematical foundations of statistics and machine learning.
- Development experience in one or more object-oriented programming languages (e.g. Python, Go, Java, C++)
- Sophisticated SQL knowledge.
- Experience with big data technologies (e.g. Hadoop, Hive, and Spark).
- Knowledge of experimental design and analysis.
- Customer-centric and pragmatic mindset. Focus on value delivery and swift execution, while maintaining attention to detail.
- Strong stakeholder management and ability to influence.
- Continuous learning and improvement mindset.