Senior Data Scientist

About the Role: At Haber, we are redefining how industries operate by blending automation with intelligent decision making. As a Data Scientist, you will play a pivotal role in designing machine learning systems that power our suite of innovative products—including Elixa, Kaiznn, Mount Fuji, and upcoming platforms like our Fiber Morphology solution. You’ll work extensively with time-series data, sensor feeds, and computer vision pipelines to uncover actionable insights in real-time. This is a high-impact, high-ownership role where your work will directly influence operational efficiency and sustainability across complex industrial environments. We value independent thinkers who are not just technical experts but also strategic problem-solvers. Key Responsibilities: Lead the full machine learning system development lifecycle: data collection, cleaning, feature engineering, modeling, deployment, and evaluation Work with time-series sensor data and computer vision systems to build predictive and anomaly detection modelsDesign and develop scalable recommendation platforms for real-time industrial decision-making Apply machine learning, data mining, and statistical techniques (e.g., regression, clustering, collaborative filtering, PCA) to a wide range of problems Design novel approaches to large-scale data analysis, including semi-structured and unstructured data Collaborate with engineering, QA, product, and operations teams to deliver end-to-end solutions Contribute to and stay up-to-date with research in areas such as machine learning, signal processing, and domain-specific optimization Make independent technical decisions and guide strategic direction based on data insights Mentor junior team members and foster a collaborative learning culture Qualification: Having 3-6 years of experience as a Data Scientist, preferably in a client facing role taking complete ownership of the Data Science lifecycle Strong programming skills in Python or R Proven experience working with time-series data and real-time data pipelines Solid foundation in supervised and unsupervised ML methods Exposure to sensor data, image processing, or computer vision techniques is a strong plus Experience with cloud environments, preferably AWS, for model deployment and monitoring Sound understanding of statistics, optimization, and data visualization Ability to work independently and drive projects from idea to execution Strong communication skills and the ability to collaborate across multidisciplinary teams A passion for solving tough, real-world problems using data What We Offer: Real-World Impact: Your models will be deployed in operational environments, not just experimental labs Ownership & Autonomy: Freedom to explore, decide, and build in a high-trust environment Diverse Tech Stack: Work on ML pipelines that combine time-series, sensor, and vision data Fast-Track Learning: Be part of a startup where learning never stops and innovation is constant Collaborative Culture: A team that supports initiative, experimentation, and personal growth Product-Minded Thinking: Your work will be tightly integrated with product and business decisions