The Machine Learning Engineer is responsible for unlocking value for the business through data. Able to identify, mature and deliver opportunities through the value funnel that demonstrate benefits. Strong understanding of the digital and data revolution and its current and future impacts on business. Able to translate business strategy into data-specific actions and interventions, to assess the benefits of new technologies and trends, and have deep understanding of digitalization and data & analytics. Strong stakeholder and change management skills and ability to partner and influence across the organizational ecosystem, including the Business, IT, TDM, Digital Centre of Excellence and Finance & Data Operations.
- Research and develop statistical learning models for data analysis
- Collaborate with product management and engineering departments to understand company needs and devise possible solutions
- Keep up-to-date with latest technology trends
- Communicate results and ideas to key decision makers
- Implement new statistical or other mathematical methodologies as needed for specific models or analysis
- Optimize joint development efforts through appropriate database use and project design
- Demonstrated skill in the use of one or more analytic software tools or languages (e.g., R, Python, Pyomo, Julia/Jump, Matlab, SAS,SQL)
- Demonstrated skill at data cleansing, data quality assessment, and using analytics for data assessment
- End-to-end system design: data analysis, feature engineering, technique selection & implementation, debugging, and maintenance in production.
- Profound understanding of skills like outlier handling, data imputation, bias, variance, cross validation etc.
- Demonstrated skill in modeling techniques, including but not limited to Predictive modeling, Supervised learning, Unsupervised learning, Machine Learning, Statistical Modeling, Natural language processing, Recommendation engines,
- Demonstrated skill in analytic prototyping, analytic scaling, and solutions integration
- Developing hypotheses and set up your own problem frameworks to test for the best solutions
- Knowledge of data visualization tools - ggplot, Dash, d3.js and Matplottlib (or any other data visualization like PowerBI, Tableau, Spotfire)
- Generating insights for a business context
- Experience with cloud technologies for building, deploying and delivering data science applications is desired (preferably in Microsoft Azure)
- Experience in Tensorflow, Keras, Theano, Text Mining is desirable but not mandatory
- Experience to work in Agile and DevOps processes.
- Bachelor or master degree in information technology, computer science, business administration or a related discipline.
- Strong stakeholder management and influencing skills. Able to articulate a vision and build support for that vision in the wider team and organization.
- Ability to self-start and direct efforts based on high-level business objectives
- Strong collaboration and leadership skills with the ability to coach and develop teams to meet new challenges.
- Strong interpersonal, communication, facilitation and presentation skills.
- Work through complex interfaces across organizational and geographic boundaries
- Excellent analytical, planning and problem solving skills
Job Experience Requirements:
- Utilize an advanced knowledge level of the Data Science Toolbox to participate in the entire Data Science Project Lifecycle and execute end-to-end Data Science project
- Work end-to-end on Data Science developments contributing to all aspects of the project lifecycle
- Should be able to handle big data, structured, unstructured data and manage the data for further analytical processes
- Have an analytical thought process and ability to process complex information.
- Keep customers as focus of analysis insight and recommendation.
- Help define business objectives/customer needs by capturing the right requirements from the right customers.
- Can take defined problems and identify resolution paths and opportunities to solve them; which you validate by defining hypotheses and driving experiments
- Can identify unstructured problems and articulate opportunities to form new analytics project ideas
- Use and understand the key performance indicators (KPIs) and diagnostics to measure performance against business goals
- Compile integrate and analyze data from multiple sources to identify trends expose new opportunities and answer ongoing business questions
- Execute hypothesis-driven analysis to address business questions issues and opportunities
- Build validate and manage advanced models (e.g. explanatory predictive) using statistical and/or other analytical methods
- Are familiar working within Agile Project Management methodologies / structures
- Analyze results using statistical methods and work with senior team members to make recommendations to improve customer experience and business result
- Have the ability to conceptualize formulate prototype and implement algorithms to capture customer behavior and solve business problems
Analyze results using statistical methods to make recommendations to improve customer experience and business results