Data Scientist I or II; MAD-BS

Location

(HTA) NCP (Hillsboro, OR)

R0128931

HITACHI HIGH-TECH AMERICA, INC.

Data Analytics/Business Intelligence

Full time

No

POSITION: Data Scientist I or II

DIVISION: Metrology and Analysis Systems Division (MAD)

COMPANY: Hitachi High-Tech America, Inc. ("HTA")

TRAVEL: Up to 5% (internationally)

REMOTE WORK: Hybrid (+50% Remote) - Remote 60% / Onsite 40%

EXPECTED PAY RANGE: Data Scientist I: $99,608 - $136,961 annually;
Data Scientist II: $121,673 - $167,301 annually

This pay range is for the position's base pay only. This position may be eligible for other compensation including bonus pay and/or allowances. Candidates will receive additional information during the interview and selection process.

Position Level: The best fit candidate selected for this position will be offered a job title/level (Data Scientist I vs. Data Scientist II) that is most appropriate after evaluating the person's education, experience, training, knowledge, skills, and abilities.

Data Scientists are responsible for the development and maintenance of Artificial Intelligence (AI) software and systems for Hitachi High-Tech America, Inc. (HTA) products.

• Hands‑on development and write algorithms in machine learning, statistical modelling, neural nets, and pattern recognition from data exploration
• Develop, train, and deploy ML models for Time‑series forecasting and anomaly detection. Classification and regression on tabular and sensor data, predictive maintenance and failure prediction
• Design end‑to‑end ML pipelines including Data ingestion, feature engineering, model training, evaluation, and deployment
• Lead and support Root Cause Analysis (RCA) investigations using data‑driven approaches
• Build frameworks for Fault Tree Analysis (FTA) and failure mode identification
• Collaborate with domain experts (engineering, operations) to translate failure patterns into ML features and models
• Design and develop Agentic AI systems capable of:
  • Autonomous reasoning over structured and unstructured data
  • Tool usage (query engines, APIs, analytics pipelines)
  • Multi‑step decision making and diagnostics workflows
• Implement LLM‑based systems with:
  • Tool‑calling frameworks
  • Retrieval‑Augmented Generation (RAG)
  • Structured outputs and validation pipelines
• Partner with cross‑functional teams (Data Engineers, Software Engineers, Domain Experts)
• Build scalable, production‑ready solutions using:
  • Python‑based ML frameworks (e.g., Tensor Flow, PyTorch, Scikit‑learn)
  • Data processing tools (Pandas, Spark, SQL)
• Deploy models and services using:
  • REST APIs (FastAPI, Flask)
  • Containerization (Docker, Kubernetes)
• Work with modern data platforms:
  • Time‑series DBs (e.g., Prometheus, InfluxDB)
  • Analytical DBs (e.g., Click House, PostgreSQL)
  • Vector DBs (e.g., Qdrant, FAISS)
• Translate business problems into technical solutions
• Creating architecture and complex designs independently and documenting them
• Integrate and test software to confirm compliance with specifications
• Developing functional specifications
• Participate in design reviews, code reviews of peers and test reviews
• Performing functional tests
• Other duties as assigned

• Master of Science degree in Data Science, Statistics, Computer Science, or similar quantitative field

• Must have at least five (5) years of practical experience in writing algorithms in Machine Learning, Statistical Modelling, Neural Nets, and Pattern Recognition from data exploration
• Five (5) years of experience in Data Science / Machine Learning
• Strong programming skills in Python
• Proven experience with:
  • Time‑series analysis and anomaly detection
  • Statistical modeling and machine learning algorithms
• Hands‑on experience with:
  • Root Cause Analysis (RCA)
  • Fault Tree Analysis (FTA) or failure modeling
• Experience working with real‑world, noisy, and large‑scale datasets
• Experience with Agentic AI / LLM systems, including:
  • Tool‑calling architecture
  • RAG pipelines
  • Prompt engineering and evaluation frameworks
• Familiarity with:
  • Distributed systems and scalable ML infrastructure
  • MLOps practices (CI/CD,…