Data Team Lead

Description

Wiliot was founded by the team that invented one of the technologies at the heart of 5G. Their next vision was to develop an IoT sticker, a computing element that can power itself by harvesting radio frequency energy, bringing connectivity and intelligence to everyday products and packaging, things previously disconnect from the IoT. This revolutionary mixture of cloud and semiconductor technology is being used by some of the world’s largest consumer, retail, food and pharmaceutical companies to change the way we make, distribute, sell, use and recycle products.

Our investors include Softbank, Amazon, Alibaba, Verizon, NTT DoCoMo, Qualcomm and PepsiCo.

We are growing fast and need people that want to be part of the journey, commercializing Sensing as a Service and enabling “Intelligence for Everyday Thing”.

We are seeking a Data Team Lead to provide technical leadership for our data organization. This individual will be responsible for guiding our data science and analytics teams in building scalable, production-ready data solutions. Reporting to the Director of Strategy, this role will ensure that our data science models and analytical insights are efficiently engineered, deployed, and maintained in production. 

This is a hands-on leadership role requiring both deep technical expertise and mentorship. The ideal candidate will bridge the gap between data science, engineering, and analytics, ensuring that our data-driven initiatives are scalable, reliable, and aligned with business needs. 

Responsibilities

• Technical Leadership: Lead the design, architecture, and implementation of scalable data engineering and ML pipelines. 
• Productionization of Data Science Models: Ensure that machine learning models are effectively deployed and maintained in production environments. 
• Collaboration & Strategy: Work closely with data scientists, analysts, and engineers to define best practices and integrate engineering rigor into data workflows. 
• Infrastructure & Pipelines: Oversee data infrastructure, storage, and processing pipelines to support real-time and batch analytics. 
• Code Quality & Best Practices: Establish coding standards, CI/CD practices, and engineering excellence within the team. 
• Mentorship & Growth: Provide technical mentorship to the team, helping data scientists and analysts improve their engineering skill sets. 
• Stakeholder Communication: Work cross-functionally with business leaders, product managers, and technical teams to align data initiatives with organizational goals. 

Requirements

• Strong software engineering background with expertise in data engineering, distributed systems, or ML infrastructure. 
• Experience deploying and maintaining machine learning models in production environments. 
• Proficiency in Python, SQL, and cloud-based data platforms (AWS, GCP, or Azure). 
• Experience with modern data stack tools (e.g., Spark, Databricks, Polars, DuckDB). 
• Familiarity with MLOps best practices (model versioning, monitoring, retraining). 
• Proven experience leading teams or mentoring engineers/data scientists. 
• Strong problem-solving skills and ability to work in a fast-paced environment. 

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