Overview Wiliot is seeking an experienced Data Scientist to join our team in one of our key locations: San Francisco, New York, or Dallas. This role focuses on developing, deploying, and optimizing machine learning models that power Wiliot’s core intelligence platform. You will work closely with engineering, product, and customer-facing teams to derive insights from IoT data and deliver high-impact ML solutions at scale. Wiliot was founded by the team that invented one of the technologies at the heart of 5G. We are building an IoT sticker that can power itself by harvesting radio frequency energy, bringing connectivity and intelligence to everyday products and packaging. Our investors include Softbank, Amazon, Alibaba, Verizon, NTT DoCoMo, Qualcomm, and PepsiCo. We are growing fast and aiming to commercialize Sensing as a Service and enable “Intelligence for Everyday Things.” Salary range: 100-180K USD annually. LI-Hybrid. Responsibilities ML Model Development: Design, build, and validate machine learning models to support applications such as anomaly detection, inventory states, and supply chain behavior on streaming and batch IoT data. Data Preparation & Feature Engineering: Collaborate with data engineers to prepare high-quality datasets, develop scalable feature pipelines, and manage training data lifecycle. Model Deployment: Implement and operationalize models using MLOps best practices, including packaging models, tracking experiments, and monitoring production performance. Collaboration & Enablement: Work with engineering and product teams to align model development with real-world use cases and enable stakeholders to leverage insights through accessible tools and visualizations. Streaming & Real-time Analytics: Contribute to real-time intelligence features using tools such as Spark Structured Streaming, Kafka, and other big data frameworks. Tooling & Automation: Build internal tools and workflows to improve experimentation speed and reproducibility; support automation of model training, evaluation, and retraining processes. Innovation & Research: Stay up-to-date with ML, AI, and IoT developments; evaluate and apply new techniques to enhance model accuracy and performance. Requirements Education Bachelor’s or master’s degree in Computer Science, Statistics, Machine Learning, or a related field. Experience 3–5 years of experience in data science roles, preferably in a technology or IoT-focused company. Proven experience developing and deploying machine learning models in production environments. Hands-on experience with Apache Spark (PySpark or Scala) for large-scale data processing. Experience working with time series or sensor data, particularly in a streaming or real-time context. Technical Skills Proficient in Python and common ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch). Strong SQL skills and familiarity with data storage formats such as Parquet and Delta. Experience with cloud platforms such as AWS, GCP, or Azure. Exposure to ML lifecycle tools like MLflow, SageMaker, or Vertex AI. Familiarity with version control systems such as Git and containerized development (e.g., Docker). Additional Skills (Bonus) Experience with Java and / or Scala. Familiarity with streaming data tools such as Kafka, Spark Structured Streaming, or Flink. DevOps / MLOps experience, including CI / CD, model monitoring, and reproducibility best practices. Exposure to Databricks or Airflow for workflow orchestration. Understanding of modern software design patterns (e.g., microservices, functional programming). Strong communication skills to bridge technical and non-technical domains. Ability to manage multiple projects and prioritize in a fast-paced environment.

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