• Correct and convert SQL queries as needed to meet project requirements.
• Gather and analyze relevant information regarding reported issues.
• Trace and identify the origin of specific issues within our data pipelines.
• Provide initial feedback to the issue reporter, ensuring clear and timely communication.
• Document all aspects of the issue, including actions taken, timelines, time spent, and any impediments encountered.
• Summarize findings and escalate issues to the core Data Engineering (DE) team for further action when necessary.
• Follow existing Standard Operating Procedures (SOPs) or develop new ones to address unique challenges.
• Make intuitive connections between data inputs and outputs to identify and resolve potential issues.
  Bonus points
• Understanding of Operational Technology (OT) domains, such as DCS / APC / soft sensor technologies from Yokogawa, Honeywell, Emerson, ABB, Siemens, AspenTech, or AVEVA, or hands-on experience working in a manufacturing plant.
• Japanese N2+ proficiency or direct experience working with Japanese clients, including Japanese-language review documents, internal notes, and client communications / IMs.
• Understanding of AI-related regulations and standards such as FDA 21 CFR Part 11, EU AI Act, and IEC 62443.
• Experience building REST APIs and web dashboards for data/model visualization.
• Knowledge of reinforcement learning, time-series forecasting, digital twins, or process simulators.
• Exposure to pre-sales & solutioning, or experience interviewing and mentoring other engineers
• For senior candidates: experience designing architecture and leading on engagements
   

• Senior Data Scientist / ML Engineer with at least 6 years of experience in industrial domains (petrochemical, chemical, pharmaceutical, energy, manufacturing), with hands-on experience developing soft sensors / virtual sensors or predictive models using real-world sensor data.
• Proven experience deploying ML models to on-premises production environments and operating them over time, including model drift monitoring and retraining — not just PoC experience. Strong understanding of Explainable AI (XAI) and the distinction between
  correlation and causation. Understands that incorrect recommendations (e.g., “reduce reactor pressure to increase conversion”) can have serious operational safety implications.
• Strong mindset toward industrial product testing and quality, including reliability,repeatability, validation, and Japan-style phase review processes.
• Strong Python ML stack: pandas, NumPy, scikit-learn, XGBoost / LightGBM.
• Experience with ML for industrial time-series data, including regression / soft sensors, sensor lag and noise handling, and time-based validation.
• Experience with AutoML, model registries, and model comparison / selection.
• Experience with Explainable AI, including SHAP / feature attribution (global and per-sample), what-if analysis, and prescriptive recommendations.
• Experience with on-premises / edge MLOps, including versioning, deployment, model monitoring, data drift detection, and retraining. 
• Experience building data pipelines from process historians, such as CSV exports, Exaquantum, or equivalent systems.
• Experience with performance sizing for multiple models running concurrently, including CPU, memory, and calculation-cycle requirements.
• Client-facing communication, aka English proficiency

OPPORTUNITIES AND CHALLENGES

• Opportunity to work in a professional, modern and energizing setting.
• Challenging projects, possibilities, cutting-edge technology, and problems.
• An open and dynamic working atmosphere that promotes the interchange of ideas, while also empowering you to work and create in your own style. Each employee's talent and accomplishments are valued, and outstanding employees are recognized and rewarded on a yearly basis.
• Opportunities for capacity development, as well as assistance with professional certification expenses, are available to those who work in the field (1 million - 5 million).
• Some advanced certifications will cover the full cost of studying and taking the exam.

ATTRACTIVE REWARDING POLICY AND WORK-LIFE BALANCE

• Review salary 1-2 times/year, with the possibility of an unexpected salary rise based on capacity an infinite number of times per year
• Policy for 13th-Month Salary Bonus and Holiday Bonuses According to Company Regulations.
• Holiday travel mode (package 5-7 million/person), team building,...
• Full participation in social insurance, as well as a yearly health checkup at a reputable hospital.
• Comprehensive visit policy for personnel and relatives: birthday, marriage, childbirth, illness, etc.
• The football club, cycling car, badminton, ... are just a few of the sports and cultural activities available.
• Allowance for classes and training awards: Learning Star of the Month,
• A chance to be the face of the year's study, the royalties...