Machine Learning / Data Mining Lead

Educational Background: Master's or Ph D in Computer Science, Machine Learning, Data Science, or related field. (Or Bachelor's with exceptional, extensive experience.) Strong theoretical foundation in machine learning, algorithms, and statistics. Experience: 7+ years in data science or ML engineering roles, with at least 2 years in a technical leadership position guiding other engineers or researchers. Proven track record of delivering AI/ML-driven features or products to market. Technical Mastery: Proficiency in Python and common ML libraries (Tensor Flow/Py Torch, scikit-learn, pandas, etc.). Experience with data pipeline tools (Spark, Kafka, or similar) and deploying models in production environments (using cloud services or on-device inference). Solid understanding of both classical algorithms and deep learning techniques. Domain Knowledge: Experience with time-series data or biosignals is a strong plus (e.g., physiological data, wearable data, or sensor streams). Familiarity with NLP for conversational agents or Q&A systems is beneficial. Problem Solving & Creativity: Exceptional ability to break down complex problems and invent original solutions. Demonstrated success in tackling hard, open-ended problems with measurable impacts (e.g., improving accuracy, reducing support tickets). Preferred Experience: Health Tech or Related Industries: Experience in healthtech, medtech, or fitness analytics, working on biometric algorithms, predictive health models, or digital therapeutics. Alternatively, experience in high-stakes data domains like finance or autonomous vehicles that require precision. Leadership & Communication: Experience presenting findings to executives or non-technical stakeholders, articulating the value of ML initiatives in terms of user impact or ROI. Full-Stack ML: Familiarity with the entire ML stack: from signal processing and feature engineering (especially for sensor data) to building customer-facing UI that displays ML results (collaborating with front-end teams). Continuous Learning: Evidence of staying current through publications, ML competitions, patents, or open-source contributions. Passion for learning and applying new technologies. Disclaimer: Naukrigulf.com is a platform connecting jobseekers and employers. Applicants should independently verify the employer's credentials. We do NOT endorse requests for money or sharing personal/bank information. For security concerns, contact abuse@naukrigulf.com. #J-18808-Ljbffr