PhD Data Scientist

Process Integration Limited; Altrincham; Hybrid; GBP 38,000 - 46,000; Full time

Benefits offered by this job

Private Medical Insurance

EAP

Company pension

Hybrid working

23 days + bank holidays

Job summary

Process Integration Limited seeks a Data Scientist with a PhD in Chemical Engineering to advance integration of domain knowledge into machine learning models for the oil and gas sector.

You will develop predictive models, design process-aware workflows, and collaborate with engineering teams to deliver data-driven improvements in a hybrid Altrincham-based role.

Qualifications

• PhD or equivalent advanced degree in Chemical Engineering with data analysis focus.
• Proficient in Python for data analysis and ML; strong stats and data visualization skills.

Responsibilities

• Develop predictive models using statistical methods and ML techniques.
• Integrate domain knowledge into models to enhance performance.
• Design data strategies and tools to support modelling in client-like environments.

Skills

Python for data analysis

Statistical modelling

Data visualization

Machine learning

Deep learning

Process data analysis

Chemical engineering knowledge

Problem solving

Communication and teamwork

Model integration

Education

PhD in Chemical Engineering

Tools

Python

pandas

NumPy

scikit-learn

PyTorch

Matplotlib/Plotly

Job description

Data Scientist (PhD, Chemical Engineering, Process Engineering & Machine Learning)

Start date: To be agreed.
Duration & hours: Full-time permanent contract, 40 hours per week.
Salary: Competitive up to £42k.

Workplace: Currently hybrid working pattern of two days in the office in Altrincham, Cheshire, followed by three days home working.

Travel Required: There is no immediate requirement for business travel, but you must be open to travel on an ad hoc basis to meet business demands.

The company: Process Integration Limited (PIL) is a visionary technology company dedicated to delivering integrated first principle and AI-driven solutions for the oil and gas industry. Our state-of-the-art, closed-loop products seamlessly merge digital twins, comprehensive optimisation, and smart execution systems while promoting operational excellence. We are committed to providing exceptional value to clients by pushing the boundaries of innovation, driving sustainable progress, and shaping the future of the energy sector through advanced technology.

We are looking for a highly skilled and innovative Data Scientist with a PhD in Chemical Engineering with a focus on full integration of domain-specific process knowledge into machine learning models. As a Data Scientist, you will play a pivotal role in advancing our chemical processes, optimising process performance, and driving product development. Your expertise in chemical engineering, machine learning, and mathematical modelling will be instrumental in shaping our future.

Qualifications & Skills

• PhD or equivalent advanced degree in Chemical Engineering, with a strong focus on data analysis, statistical
  modelling, and computational techniques.
• Proficiency in Python for data analysis and machine learning. Strong understanding of statistical methods,
  data manipulation, and data visualisation libraries (e.g., pandas and NumPy, scikit-learn, PyTorch,
  matplotlib/plotly).
• Utilise process/data analysis techniques to assess the potential of existing processes and propose alternative
  production methods and operating parameters to improve process performance.
• Strong analytical and problem-solving skills to tackle complex challenges related to chemical processes and
  data analysis, particularly from a data science and machine learning point of view. Ability to break down
  problems into manageable components and develop innovative solutions using data-driven approaches.
• Understanding and hands-on experience in solving complex data-oriented issues, often requiring literature
  research.
• Modelling and optimisation of refinery, petrochemical, and chemical processes.
• Understanding of mathematical modelling and optimisation approaches for engineering purposes (particularly
  non-linear oil and gas processes).
• Willingness to engage in continuous learning and professional development activities to enhance skills and
  expertise.
• Attention to Detail: Meticulous attention to detail to ensure accuracy and reliability of data analysis and
  modelling results. Ability to identify and address potential sources of error or bias in data and methodologies.
• Excellent critical thinking skills and a passion for innovation.
• Effective communication and teamwork abilities.
• Strong background in machine learning, including experience with deep learning, regression, and data-driven
  modelling.

Responsibilities:

• Develop predictive models using a variety of techniques including traditional statistical methods, machine learning, LLMs/RAG/Agents.
• Ensure these models are not only technically competent but also fully integrated with domain-specific process knowledge to enhance performance and application.
• Focus on the readiness of models for practical implementation in client-specific environments, ensuring they are robust, scalable, and adaptable.

Process-Oriented Data Strategy:

• Design and implement data collection and analysis strategies that leverage deep process understanding to optimise the accuracy and utility of models.
• This involves selecting the right type of data, pre-processing methods, and data enrichment techniques that reflect process intricacies.
• Ensure these strategies are conducive to real-world applications, particularly in preparing models for integration into client projects.
• Innovate and develop in-house tools and software solutions that facilitate the modelling, simulation, and analysis of complex chemical processes.
• These tools should support the seamless integration of new AI technologies and process engineering principles.
• Design these tools to be flexible and effective in deploying developed models into client systems, enhancing the operational impact.

Stakeholder Engagement and Solution Implementation:

• Work directly with colleagues in technical and business teams to translate complex model outputs into actionable business insights.
• Present findings in a way that is accessible to non-experts and can influence strategic decisions.

Continuous Learning and Skill Enhancement:

• Keep abreast of the latest developments in data science, AI, and chemical engineering to continually refine and enhance modelling techniques.
• Participate in professional development opportunities and contribute to the knowledge base of the team.
• Apply new knowledge to improve model implementation processes and ensure the solutions remain at the forefront of technology and industry practices.
• Competitive salary and benefits package including Private Medical Insurance, EAP, company pension, hybrid working and holiday entitlement starting with (FTE) 23 days + bank holidays.
• Opportunity to work on cutting-edge projects with industry leaders.
• Professional development and growth opportunities.
• Collaborative and inclusive work environment.

There is a two-stage interview process first with a phone interview. Then the final interview with case study presentation requirements which will be conducted in person at the company office location in Altrincham.