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Datatech

Capacity Planning Data Scientist

Datatech Middlesex
32 - 40 hour


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    Datatech

    Capacity Planning Data Scientist

    Datatech Middlesex
    32 - 40 hour
    Status Open
    Apply now

    Apply on the employer's website


    What we ask

    Education

    No minimum education required

    What we offer

    Hours
    32 to 40 hours per week
    Employment type
    permanent

    Job description

    Capacity Planning Data Scientist
    Location: Middlesex (3 days onsite, 2 days from home)
    Salary: Negotiable depending on experience
    Job Ref: J13078

    A market leading global logistics organisation is seeking a highly motivated Airline Capacity Planning Data Scientist who wants to apply their work in a real, high impact environment.
    Your work will directly influence how capacity is planned, optimised, and delivered within a complex, high volume operational setting.

    This role sits at the intersection of data science and operations research, with a strong focus on simulation, modelling, and real world system optimisation.
    You will work with large scale datasets to build machine learning models and develop advanced simulation tools that support critical planning decisions.

    The role:
    ·Design and build machine learning models to forecast demand, utilisation and system performance
    ·Work with large scale, time series datasets to engineer features and improve model accuracy
    ·Apply robust validation, tuning and model selection techniques to deliver reliable outputs
    ·Build and calibrate discrete event simulation (DES) models of complex operational systems to stress test capacity under different demand scenarios
    ·Carry out scenario analysis and what if modelling to support planning and investment decisions
    ·Translate complex analytical outputs into clear, actionable insights for operational stakeholders
    ·Develop tools and dashboards to monitor system performance and support decision making
    ·Work closely with operational teams to ensure models reflect real world constraints and behaviours

    Experience required:
    ·Master's degree (or equivalent) in Data Science, Statistics, Operations Research, Computer Science, Mathematics, or a closely related quantitative discipline
    ·Strong experience in a Data Scientist or Operational Research focused role working with large, complex datasets
    ·Solid understanding of machine learning techniques such as gradient boosting, random forests and time series modelling
    ·Experience building and deploying models used in real world decision making (e.g. ensemble methods, regularised regression)
    ·Advanced proficiency in R, including tidyverse, data.table, caret or tidymodels, xgboost and Shiny
    ·Strong SQL skills with experience querying large scale relational databases such as Azure SQL or PostgreSQL
    ·Experience working with high volume or time based data, including feature engineering
    ·Experience with simulation, discrete event simulation, or operations research techniques is highly desirable
    ·Ability to communicate complex analysis clearly to non-technical stakeholders
    ·Comfortable working in fast paced, operational environments
    ·Proactive, hands on approach with the ability to take ownership of problems end to end

    If you want to work on complex systems where your output is actually used and matters, this is a genuinely strong opportunity to do that.

    Please note we can only accept applications from those with current and full UK working rights for this role, as sponsorship is not available.

    If this sounds like the role for you then please apply today.

    Alternatively, you can refer a friend or colleague through our referral scheme. For each successful placement, you will be eligible for our reward scheme with no limit on referrals.
    Datatech is one of the UK's leading recruitment agencies in analytics and the host of the Women in Data event. For more information, visit (url removed) <(url removed)
    Apply now

    Apply on the employer's website

    Apply now

    Apply on the employer's website


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