html About arenaflex Pioneering the Future of Intelligent Computing arenaflex is a global leader in cuttingedge technology delivering innovative hardware software and services that empower millions of users worldwide Our mission is to blend seamless user experiences with powerful artificial intelligence creating products that feel intuitive responsive and truly personal As part of our ongoing commitment to push the boundaries of AI research arenaflex invests heavily in the development of multimodal foundation modelssystems that can understand and generate text images video and moreall within a single unified framework Why This Role Matters In todays fastevolving AI landscape the ability to evaluate refine and scale multimodal models is a critical differentiator arenaflexs Data Quality DAQ team is expanding its expertise to include rigorous scientific assessment of these models ensuring they meet the highest standards of performance fairness and reliability As a Remote PartTime Data Scientist you will be at the heart of this effort collaborating with worldclass ML engineers data analysts and infrastructure specialists to shape the next generation of intelligent products Role Overview This position blends deep technical research with practical data engineering You will design and execute evaluation pipelines develop novel benchmarking methodologies and contribute to the creation of highquality training datasets While the role is parttime and fully remote you will work closely with crossfunctional teams across multiple time zones participating in regular virtual syncups code reviews and design discussions Key Responsibilities Model Evaluation Benchmarking Design implement and maintain rigorous evaluation frameworks for largescale multimodal foundation models such as SAM LLAMA LLaVA CGPT4V and others Data Pipeline Development Build robust data ingestion cleaning and transformation pipelines that feed highquality data into model training and validation cycles Statistical Analysis Reporting Conduct detailed statistical analyses of model performance error patterns and bias metrics produce clear actionable reports for engineering and product stakeholders Experiment Design DOE Plan and execute systematic experiments including ablation studies and largescale user simulations to uncover insights that drive model improvements Collaboration Knowledge Sharing Partner with ML engineers data scientists and infrastructure teams to integrate evaluation tools into the broader ML workflow mentor junior team members on best practices Feature Specification User Impact Modeling Translate datadriven findings into feature specifications that anticipate user experience outcomes and guide product roadmaps Tool Development Create reusable software utilities for data visualization model diagnostics and automated reporting using Python and associated scientific libraries Essential Qualifications Bachelors degree in Computer Science Statistics Applied Mathematics or a related quantitative field Minimum of 3 years of skilled experience in data science machine learning or AI research preferably within a hightech or researchintensive environment Strong foundation in machine learning theory computer vision and deep learning architectures Demonstrated expertise in evaluating complex AI models including experience with performance metrics error analysis and bias detection Proficiency in Python programming comfortable with libraries such as Jupyter Pandas NumPy Matplotlib and scientific computing tools Handson experience with deep learning frameworks e g PyTorch TensorFlow JAX for model training and inference Excellent written and verbal communication skills with a proven ability to convey technical concepts to diverse audiences Preferred Qualifications Additional Skills Masters or Ph D in a quantitative discipline with a focus on AI computer vision or multimodal learning Experience working on largescale foundation models e g SAM LLAMA LLaVA CGPT4V and familiarity with their architectural nuances Background in statistical experiment design hypothesis testing and causal inference Knowledge of data annotation pipelines crowdsourcing platforms and quality assurance processes for training data Familiarity with cloudbased ML infrastructure AWS GCP Azure and containerization technologies Docker Kubernetes Track record of publishing research findings in peerreviewed conferences or journals Ability to thrive in a remote parttime setting while maintaining high productivity and meeting project deadlines Core Skills Competencies Analytical Rigor Ability to dissect complex model behaviors identify root causes of performance gaps and propose datadriven remediation strategies Collaboration Strong teamwork

📌 Remote PartTime Data Scientist Multimodal Foundation Model Evaluation and Data (India)
🏢 vmysmartpros
📍 India