Saaksshi Jilhewar
Lead Data Scientist @Neuralix Inc
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WORK HISTORY
Lead Data Scientist @Neuralix Inc
Directed multiple projects from proof-of-concept to production, working closely with clients to capture requirements, architect ML-driven solutions, and deliver systems that achieved measurable business impact within tight timelines.•Designed and deployed time-series forecasting, anomaly detection, and optimization models using Python and PyTorch, integrating them into enterprise-scale decision platforms that supported multi-site deployment.•Engineered robust data pipelines and automated feature engineering workflows, enabling real-time monitoring of operational data streams, reducing latency in analytics, and improving scalability of model deployment.•Delivered customized AI solutions across diverse industries, adapting shared frameworks to unique operational datasets in energy, logistics, mining, and steel manufacturing, ensuring both reusability and domain relevance.Winner of Shell’s Global Changemakers of Tomorrow competition, where I led the project DeCarbonix, an AI-first fleet management platform recognized among the top global solutions for its innovative approach to decarbonization.
EDUCATION
Carnegie Mellon University's College of Engineering
Master of Science - MS
Vellore Institute of Technology
Bachelor of Technology - BTech
ABOUT SAAKSSHI JILHEWAR
I am a Data Scientist with expertise in large language models, retrieval-augmented generation, deep learning, and constraint optimization. My work focuses on building AI systems that are scalable, reliable, and directly impactful for customers. Beyond the technical side, I have experience managing projects end to end. This includes working with clients directly, handling CRM tools, engaging stakeholders, and ensuring that each phase of delivery from research and prototyping to deployment and monitoring creates measurable business value. I thrive in fast-paced and challenging environments where I can combine technical innovation with customer focus, particularly in industries like chemicals, energy, and enterprise AI.
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