we are looking for a AI Applied Scientist – Insights & Recommendations.
As AI Applied Scientist, youll be at the intersection of advanced technology and meaningful impact with clear business cases. You will have access to real-time (RT) big data from multiple Sensors & Data sources, and a chance to discover, explore research, and develop cutting-edge models that directly impact industrial manufacturers around the globe. As part of the Applied AI/ML Science team , you will have the opportunity to continuously grow and learn while building and deploying advanced models directly into our products, and stay at the forefront of the world's technologies, witnessing firsthand their impact on our customers.
A Day In Your Life:
Own the algorithm lifecycle from problem definition and data analysis to prototyping and delivering production-ready models.
Combine classic methods with inference techniques: statistics, Time series, Deep learning, anomaly detection, Recommendation Systems, Transformers, and Inference to extract data-driven insights.
Research, design, and build Agentic applications on top of sensor time-series data, textual data, images, ML applications, and other various data sources.
Engage with customers and collaborate with our product team to develop innovative solutions utilizing new types of data.
Leverage modern technologies: work with cloud-based big data platforms for storage, distributed processing, LLMs and agents (GPT, Claude, Gemini), defining & building Gurdrails, reasoning chain as function call, planning, ML-based vectorization and embeddings, and stream analysis.
As AI Applied Scientist, youll be at the intersection of advanced technology and meaningful impact with clear business cases. You will have access to real-time (RT) big data from multiple Sensors & Data sources, and a chance to discover, explore research, and develop cutting-edge models that directly impact industrial manufacturers around the globe. As part of the Applied AI/ML Science team , you will have the opportunity to continuously grow and learn while building and deploying advanced models directly into our products, and stay at the forefront of the world's technologies, witnessing firsthand their impact on our customers.
A Day In Your Life:
Own the algorithm lifecycle from problem definition and data analysis to prototyping and delivering production-ready models.
Combine classic methods with inference techniques: statistics, Time series, Deep learning, anomaly detection, Recommendation Systems, Transformers, and Inference to extract data-driven insights.
Research, design, and build Agentic applications on top of sensor time-series data, textual data, images, ML applications, and other various data sources.
Engage with customers and collaborate with our product team to develop innovative solutions utilizing new types of data.
Leverage modern technologies: work with cloud-based big data platforms for storage, distributed processing, LLMs and agents (GPT, Claude, Gemini), defining & building Gurdrails, reasoning chain as function call, planning, ML-based vectorization and embeddings, and stream analysis.
Requirements:
M.Sc., or Ph.D. in Electrical Engineering, Computer Science, Physics, Mathematics, or a related field.
5+ years of experience in ML applications in modeling Time-series data, & ML-based vectoring, and Embeddings for insight & recommendations systems (Transformers included) – Mandatory
2+ years of experience in LLM and Agentic applications, Eval tools, methods, and workflows (HITL, LLM-as-a-judge, deterministic, Metrics & Embeddings), Fine-Tuning, and AI workflows and lifecycle (e.g. LangSmith, CrewAI, LangGraph, Embedding (BERT, w2v, FastText, FastEmbed, GloVe, etc.), Tokenizations (GPT. TikToken, Token validators, etc.), & Vector Similarities & search methods
Proficiency in Python for model development, deployment, and monitoring.
Experience working on Agile teams with a passion for fast iterations, feedback, and continuous learning.
Proven ability to collaborate with diverse, cross-functional groups, including product managers, infrastructure, and data engineering teams.
The ability to translate research into scalable, production-ready solutions.
Experience in anomaly detection and AI/ML – an advantage.
Experience in feature engineering-based signal processing – an advantage
Experience in optimizing costs of API calls – Nice to have
M.Sc., or Ph.D. in Electrical Engineering, Computer Science, Physics, Mathematics, or a related field.
5+ years of experience in ML applications in modeling Time-series data, & ML-based vectoring, and Embeddings for insight & recommendations systems (Transformers included) – Mandatory
2+ years of experience in LLM and Agentic applications, Eval tools, methods, and workflows (HITL, LLM-as-a-judge, deterministic, Metrics & Embeddings), Fine-Tuning, and AI workflows and lifecycle (e.g. LangSmith, CrewAI, LangGraph, Embedding (BERT, w2v, FastText, FastEmbed, GloVe, etc.), Tokenizations (GPT. TikToken, Token validators, etc.), & Vector Similarities & search methods
Proficiency in Python for model development, deployment, and monitoring.
Experience working on Agile teams with a passion for fast iterations, feedback, and continuous learning.
Proven ability to collaborate with diverse, cross-functional groups, including product managers, infrastructure, and data engineering teams.
The ability to translate research into scalable, production-ready solutions.
Experience in anomaly detection and AI/ML – an advantage.
Experience in feature engineering-based signal processing – an advantage
Experience in optimizing costs of API calls – Nice to have
This position is open to all candidates.
