Engineering Manager, Machine Learning
hace 1 mes
Data and Machine Learning (ML) are revolutionizing the way of doing business at a global scale. sennder is a European digital freight forwarder with a data-centric problem-solving approach to build the next generation of supply chain and road logistics services. Do you want to help us to shape the future?
We are looking for a (Senior) Engineering Manager - Machine Learning o join our central Machine Learning Engineering team as part of sennder’s Data department. The department’s mission is to “Relentlessly build exceptional value-adding products that inspire data-centricity in everything sennder does”. We’re a large, diverse team of ML/data engineers, data scientists, analysts and technical product people that are passionate by such mission. We want to attract and train world-class talent to form a incredible group that aim to provide you with the most productive and growth-friendly time of your career.
**All Data dept. teams are multidisciplinar (e.g.**: data engineering, MLOps, Data Science). The teams’ scope ranges from creating a unique technological backbone for our data platform to developing advanced predictive and prescriptive analytical services. Our proprietary data platform enables our operation teams to work in a distributed analytics-as-a-service ecosystem where everyone is empowered to be data driven in every decision they make. Our predictive and prescriptive analytics services bring us an unprecedented competitive advantage in terms of business automation across the industry. The obtained insights are then translated in a better customer experience, enabling a scalability flywheel data and revenues grow exponentially with each
other.
Every day, we acquire 3M+ new real-time data points (augmenting by the day) about the cross-region road logistics industry in Europe. This data is used to build the future of logistics marketplaces where pricing optimization, load-to-carrier recommendation, load search and network optimization happen in an automated fashion.
Can you even imagine where we can go with your help? Let’s #keepOnTrucking... together
Your Mission
- As a people manager: Hire, manage, engage (including by organising team building events), coach, grow and retain exceptional talent in the areas of machine learning, data science and MLOps;
- As a leader: foster an environment of trust, self-organization, empowerment, failure-friendly but mandatory
- learnings towards to both personal and professional growth;
- As a individual contributor, you will hands-on address some of the following challenges (whenever time allows and promoting a culture of leading by example):
- Define the new state-of-the-art for machine learning engineering in the road logistics services;
- Prototype and subsequently operationalize innovative, data-intensive, end-to-end machine-learning-based decision engines, following the latest best practices on MLOps;
- Outline and develop health and performance monitoring tools (MLOps) of data pipelines and the machine learning services in production;
- Design and improve heterogeneous, asynchronous and high-performance large-data processing pipelines from/to multiple sources/destinations;
- Enforce the best principles in ML System Design by balancing the feedback loop on data exploitation and data acquisition, follow the 80/20 ruling, focus on the right metric in every design decision and once a shippable amount of value is created, go live, evaluate, learn and iterate;
Your Profile
- Highly motivated with excellent communication and strong interpersonal skills;
- Proven experience in people management (onboarding, offboarding, individual development plans, perfor-mance review & management, hiring/letting go, 1-on-1s, continuous feedback, organizing team building events) in the tech industry (2+ yrs.);
- Proven experience in hands-on contribution to machine learning engineering teams in the tech industry (2+ yrs.);
- M.Sc or PhD in a quantitative field and/or working experience as a Data Scientist, Machine Learning Engineer or Data Engineer (MLOps);
- Teamaholic. We don’t believe in super-heroes but rather in super-teams: teams that own products and are a single unit of work : )
- Experience with working on Machine Learning projects to learn from structured data, as well as, deep knowledge on statistics;
- Solid Python and software engineering skills, including best practices like CI/CD and Git;
- Experience with Agile philosophies (e.g.: Scrum, Scrumban, Kanban, XP) and project management tools (e.g.: JIRA);
- Basic understanding of machine learning product lifecycle and the commonly associated components (MLOps): Experimental Environment (e.g.: Jupyter Notebook, MLflow) Workflow management (e.g.: Air-flow), Feature Stores (e.g.: Feast), DataOps/Pipelines (e.g.: Kafka), Model Deployment (e.g.: Terraform), Testing, Serving (e.g.: Docker, Flask). and Monitoring (e.g.: Datadog), Model Repository (e.g.: DVC) \*
- Fluent written and verbal communication in E
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