Lead Machine Learning Engineer
Data
Lead Machine Learning Engineer
Warszawa
Type of work
Undetermined
Experience
Senior
Employment Type
B2B
Operating mode
Office
Tech stack
Machine Learning
Python
SQL
Git
Job description
Online interview
Job Description
As a key member of the Equinix’s IT Data Science team, you will lead multiple machine learning projects and build our ML platform. While working with stakeholders to understand their needs, you’ll be responsible for managing, planning, building and operating innovative machine learning solutions that are truly connected with company’s top-line goals. The data engineering and architecture teams will be your partners to build a solid ML platform to deploy our models. You will lead a team of data scientists and machine learning engineers to build a state-of-the-art ML platform and deliver use cases, owning the design, development, implementation and deployment of our solutions.
Job Profile Summary
Our growing IT Analytics and Data Science team is spread in US, Poland, India and Singapore with a diverse set of skills and backgrounds. We work with our internal customer in departments like Sales, Marketing, Finance, Operations and more, to deliver machine learning solutions to critical data-driven problems directly connected to company’s top-level goals.
We have a variety of datasets generated by our internal systems, that includes tabular/panel data and text (and even videos) and datasets acquired from third-party providers. We have several ongoing projects related to revenue, retention, growth, risk mitigation and a vast green field to explore new ideas. To deliver such solutions we use multiple machine learning tools like classification, regression, clustering, time series, anomaly detection, NLP, graph models and constantly exploring new ones.
We are building our ML platform considering MLOps best practices to increase productivity and reliability across several machine learning use cases. We use GCP (Google Cloud Platform) and its cloud components to architect our solutions, using mostly python, SQL and popular data science libraries for all our work while keeping flexibility to try new technologies along the way.
Responsibilities
- Deliver ML projects: Initiate, lead and improve data science use cases in multidisciplinary teams, from idea to code in production. Optimize the data science lifecycle of our projects, proactively spotting impediments and improvements. Evangelize a strong vision and roadmap on delivering value while developing new capabilities to enable business teams with data
- ML engineering and operations: Research, design and build a ML platform that will sustain all our models’ full life cycle. Interact with data engineering and architecture teams to execute and optimize the way we consume data and deploy models in production. Roadmap includes feature store, model monitoring and other MLOps concepts
- People management: Lead team of 5+ data Scientists and ML engineers in Poland and work with cross functional teams located in US, India, Singapore and other parts of EMEA
- Engage with internal customers: Conduct requirements workshops with stakeholders to identify and deeply understand business problems. Communicate complex concepts and the results of the analyses in a clear and effective manner through creative visualization and presentation skills
- Data expertise: Work with large volumes of data; extract and manipulate large datasets using tools such as Python and SQL. Leverage a state-of-the-art ML platform to train and deploy models. Drive scalability, reliability and efficiency across projects
- Standardization: You will be fully involved in optimization of several processes, coming up with standardization for various approaches in solving use cases/problems and making sure that multiple projects are following the defined frameworks
- Collaboration: Coordinate and work with cross functional teams, sometimes located at different locations
Required skills
- CS fundamentals: You have earned at least a B.S. (MS / PhD desired) in Computer Science or related degree, and you have a strong ethos of continuous learning
- Software engineering: You have 7+ years of professional software development experience with Python and SQL using version control (GIT), with good analytical & debugging skills
- Machine learning: You have 3+ years of experience with machine learning modeling. You have worked with python libraries like pandas, numpy, scikit learn, tensorflow, keras, in developing different ML/AI solutions. Knowledge about methods such as classification, regression, time series, NLP, anomaly detection, clustering and other common ML tools. Experience with model interpretability (lime, shapley and similar) is a plus
- Cloud and ML environments: You have working knowledge of cloud environments like GCP (preferred), AWS or other cloud data platforms. Understanding of different cloud tools and components to build ML solutions (Seldon, MLflow, Kubeflow, Vertex AI or similar)
- Machine learning operations (MLOps): Practical experience in MLOps use cases such as: Feature Store, Model monitoring, Data and model versioning, CI/CD, Github Actions
- Data manipulation: Deep interest in understanding the datasets related to the projects, spot and communicate data quality issues, knowledge of different feature engineering techniques to feed models with the best possible data
- People management: You have 1+ years of experience leading and/or managing teams of data scientists, providing mentorship and guidance to the team
- Project management: You demonstrate excellent project and time management skills, exposure to scrum or other agile practices in JIRA. Deep understanding of CRISP-DM or similar methodologies
- Fluent in English
Desired skills
- ML use cases: Experience in building use cases around one or more of the following topics – information & network security, e-mail classifiers, fraud detection
- AutoML: Experience and interest to work with AutoML software (like H2O Driverless AI, GCP AutoML or others)
- Graph ML: Experience in use cases that leverage graph data related technologies and use graph-specific machine/deep learning to build models on top of it. Experience with Link Prediction problem is a big plus
- Deep learning: Experience with deep learning and knowledge of different architectures in the fields of supervised, unsupervised and/or reinforcement learning
Successful candidate will
- Be a talent multiplier who gets the team around them to excel
- Be persistent, creative and driven to get results relentlessly
- Exhibit a strong backbone to challenge the status quo, when needed
- Exhibit a high level of curiosity, keeping abreast of the latest trends & technologies
- Show pride of ownership and strive for excellence in everything undertaken
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