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Qylis is an engineering organization collaborating directly with clients to address challenges using the latest technologies. Our focus is on joint code development with clients’ engineers for cloud-based solutions, accelerating organizational progress. Working with product teams, partners, and open-source communities, we contribute to open source, striving for platform improvement. This role involves creating impactful solution patterns and open-source assets. As a team member, you’ll collaborate with engineers from both teams, applying your skills and creativity to solve complex challenges and contribute to open source, while fostering professional growth.

Responsibilities:

  • Researching and developing production-grade models (forecasting, anomaly detection, optimization, clustering, etc.) for global cloud business by using statistical and machine learning techniques.
  • Manage large volumes of data, and create new and improved solutions for data collection, management, analyses, and data science model development.
  • Drive the onboarding of new data and the refinement of existing data sources through feature engineering and feature selection.
  • Apply statistical concepts and cutting-edge machine learning techniques to analyze cloud demand and optimize data science model code for distributed computing platforms and task automation.
  • Work closely with other data scientists and data engineers to deploy models that drive cloud infrastructure capacity planning.
  • Present analytical findings and business insights to project managers, stakeholders, and senior leadership and keep abreast of new statistical / machine learning techniques and implement them as appropriate to improve predictive performance.
  • Oversees the analysis of data and leads the team in identifying trends, patterns, correlations, and insights to develop new forecasting models and improve existing models.
  • Leads collaboration among team and leverages data to identify pockets of opportunity to apply state-of-the-art algorithms to improve a solution to a business problem.
  • Consistently leverages knowledge of techniques to optimize analysis using algorithms.
  • Modifies statistical analysis tools for evaluating Machine Learning models. Solves deep and challenging problems for circumstances such as when model predictions are not correct, when models do not match the training data or the design outcomes when the data is not clean when it is unclear which analyses to run, and when the process is ambiguous.
  • Provides coaching to team members on business context, interpretation, and the implications of findings. Interprets findings and their implications for multiple businesses, and champions methodological rigour by calling attention to the limitations of knowledge wherever biases in data, methods, and analysis exist.
  • Generates and leverages insights that inform future studies and reframe the research agenda. Informs both current business decisions by implementing and adapting supply-chain strategies through complex business intelligence.

Qualifications:

  • M.Sc. in Statistics, Applied Mathematics, Applied Economics, Computer Science or Engineering, Data Science, Operations Research or similar applied quantitative field
  • 6+ years of industry experience in developing production-grade statistical and machine learning code in a collaborative team environment.
  • Prior experience in machine learning using R or Python (scikit / numpy / pandas / statsmodel).
  • Prior experience in time series forecasting.
  • Prior experience with typical data management systems and tools such as SQL.
  • Knowledge and ability to work within a large-scale computing or big data context, and hands-on experience with Hadoop, Spark, DataBricks or similar.
  • Excellent analytical skills; ability to understand business needs and translate them into technical solutions, including analysis specifications and models.
  • Experience in machine learning using R or Python (scikit / numpy / pandas / statsmodel) with skill level at or near fluency.
  • Experience with deep learning models (e.g., tensorflow, PyTorch, CNTK) and solid knowledge of theory and practice.
  • Practical and professional experience contributing to and maintaining a large code base with code versioning systems such as Git.
  • Creative thinking skills with emphasis on developing innovative methods to solve hard problems under ambiguity.
  • Good interpersonal and communication (verbal and written) skills, including the ability to write concise and accurate technical documentation and communicate technical ideas to non-technical audiences.