Data Scientist II

Honeywell Honeywell · Industrial · Pittsford, NY +1

This role involves designing, building, evaluating, and integrating AI/ML models and analytical components, with a focus on generative AI, LLMs, and multi-modal embeddings for security applications. The data scientist will be responsible for data engineering, developing new generative AI systems, and deploying analytical systems that improve security outcomes, potentially involving agentic patterns and frameworks.

What you'd actually do

  1. Participate in extending product capabilities through development and deployment of analytical systems leveraging data mining, generative AI, and ML techniques.
  2. Interact with 3rd parties to evaluate integration feasibility and licensing of technologies and analytic components
  3. Design, prototyping, and implementation of new data-centric solutions
  4. Effectively communicate and collaborate with local teams, international teams, and 3rd parties
  5. Contribute to build versus buy decisions

Skills

Required

  • BS or MS in an appropriate technology field (Computer Science, Statistics, Applied Math, etc.)
  • 2 years of experience in modern advanced analytical tools and programming languages, including Python with scikit-learn
  • Some experience with exploratory data analysis
  • Some experience with ETL operations sourcing data from SQL, REST APIs, and flat files
  • Basic experience with data visualization technologies, such as Power BI, Tableau, matplotlib, Excel, etc.
  • 2 years of experience in traditional programming languages such as Python, JavaScript, TypeScript, C plus plus or C#
  • Comfortable in Windows and Linux environments

Nice to have

  • Some exposure to building generative AI applications leveraging embeddings, LLMs, VLMs, vector databases, data source APIs, and agentic patterns/frameworks
  • Basic understanding of various deployment topologies including on-premises, hybrid, and cloud for production generative AI applications
  • Basic understanding of building predictive and decision-making AI applications.
  • Some exposure to using cloud services from AWS, Azure, or GCP to develop solutions.
  • Awareness of computer vision and image/video analysis, including object detection, recognition, tracking, and identification
  • Some exposure with application of data mining algorithms and statistical modeling techniques such as clustering, classification, regression, decision trees, neural networks, SVMs, anomaly detection, recommender systems, pattern discovery, and text mining

What the JD emphasized

  • 2 years of experience in modern advanced analytical tools and programming languages, including Python with scikit-learn
  • 2 years of experience in traditional programming languages such as Python, JavaScript, TypeScript, C plus plus or C#
  • building generative AI applications leveraging embeddings, LLMs, VLMs, vector databases, data source APIs, and agentic patterns/frameworks
  • computer vision and image/video analysis, including object detection, recognition, tracking, and identification
  • application of data mining algorithms and statistical modeling techniques such as clustering, classification, regression, decision trees, neural networks, SVMs, anomaly detection, recommender systems, pattern discovery, and text mining

Other signals

  • develop new innovative solutions
  • deploy state-of-the-art AI-ML models
  • leverage physical access control system datasets
  • integrating systems together
  • mashing-up datasets
  • leveraging state-of-the-art data mining, generative AI, and ML techniques
  • data engineering
  • leveraging closed and open-source LLMs
  • text and multi-modal embedding models
  • development of new generative AI systems and ML models
  • analytical systems that improve forensic and real-time security outcomes
  • extend the capabilities of our core product ecosystems
  • development and deployment of analytical systems leveraging data mining, generative AI, and ML techniques
  • evaluate integration feasibility and licensing of technologies and analytic components
  • Design, prototyping, and implementation of new data-centric solutions
  • build versus buy decisions
  • building generative AI applications leveraging embeddings, LLMs, VLMs, vector databases, data source APIs, and agentic patterns/frameworks
  • deployment topologies including on-premises, hybrid, and cloud for production generative AI applications
  • building predictive and decision-making AI applications
  • application of data mining algorithms and statistical modeling techniques