Machine Learning Developer

Synechron; Greater London; Hybrid; GBP 65,000 - 95,000; Full time

Job summary

Synechron seeks a mid-level Data Scientist to build and maintain forecasting models. You will work with time series methods (ARIMA, exponential smoothing) and gradient-boosted models (XGBoost), developing solutions in Microsoft Fabric for production deployment.

You will ingest, clean, and transform structured time-indexed data, evaluate model performance with metrics like MAPE and backtesting, and collaborate with the ML team to translate forecasting requirements for business stakeholders.

Qualifications

• 4+ years of experience in data science, quantitative analytics, or applied statistics.
• Hands-on experience building and tuning ARIMA and XGBoost models.
• Proficiency in Python (pandas, scikit-learn) and SQL; familiarity with PySpark is a plus.
• Experience with Microsoft Fabric or similar data platforms is desirable.
• Exposure to financial services or macroeconomic data is a plus.

Responsibilities

• Design, build, and validate time series forecasting models (ARIMA, exponential smoothing) and ML models (XGBoost).
• Ingest, clean, and transform structured, time-indexed data using data pipelines in Fabric.
• Evaluate model performance using statistics and ML metrics (MAPE, backtesting) and iterate on features.
• Deploy and monitor models in production within Fabric pipelines.
• Collaborate with ML teams and stakeholders to translate forecasting needs into modeling solutions.
• Document modelling assumptions, validation results, and governance artifacts.
• Stay updated on advances in time series forecasting and ML methods.
• Support the wider ML team in operationalizing model outputs for business use.

Skills

Time series forecasting

ARIMA

XGBoost

Python

PySpark

SQL

Education

Bachelor's or Master's in Data Science/Statistics/Mathematics/Computer Science

Tools

Microsoft Fabric

Job description

Work Type: Hybrid / Open for full-time & Contract

About Company:

At Synechron, we believe in the power of digital to transform businesses for the better. Our global consulting firm combines creativity and innovative technology to deliver industry-leading digital solutions. Synechron’s progressive technologies and optimization strategies span end-to-end Artificial Intelligence, Consulting, Digital, Cloud & DevOps, Data, and Software Engineering, servicing an array of noteworthy financial services and technology firms. Through research and development initiatives in our FinLabs we develop solutions for modernization, from Artificial Intelligence and Blockchain to Data Science models, Digital Underwriting, mobile-first applications and more. Over the last 20+ years, our company has been honored with multiple employer awards, recognizing our commitment to our talented teams. With top clients to boast about, Synechron has a global workforce of 14,700+ and has 55 offices in 20 countries within key global markets. For more information on the company, please visit our website or LinkedIn community.

Diversity, Equity, and Inclusion

Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal opportunity workplace and an affirmative-action employer. Our Diversity, Equity, and Inclusion (DEI) initiative ‘Same Difference’ is committed to fostering an inclusive culture – promoting equality, diversity and an environment that is respectful to all. We strongly believe that a diverse workforce helps build stronger, successful businesses as a global company. We encourage applicants from across diverse backgrounds, race, ethnicities, religion, age, marital status, gender, sexual orientations, or disabilities to apply. We empower our global workforce by offering flexible workplace arrangements, mentoring, internal mobility, learning and development programs, and more.

All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant’s gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law.

About this role:

We are looking for a mid-level Data Scientist (4+ years of experience) to help build and maintain forecasting models. You will work with time series methods such as ARIMA and gradient-boosted models such as XGBoost, developing solutions natively within Microsoft Fabric. This role suits someone who enjoys translating real-world financial data into reliable, production-ready forecasts.

What You Will Do:

• Design, build, and validate time series forecasting models (ARIMA, exponential smoothing) and machine learning models (XGBoost) for various business forecasting processes.
• Utilize data pipelines within Microsoft Fabric to ingest, clean, and transform structured and time-indexed data.
• Evaluate model performance using appropriate statistical and ML metrics (MAPE, backtesting) and iterate on feature engineering and model selection.
• Suggest and implement recommendations to enhance model outcomes/additional models as data structures and business needs evolve.
• Deploy and monitor models in production, ensuring forecasts refresh reliably on schedule within Fabric pipelines
• Partner with the ML team and other stakeholders to translate forecasting requirements into modelling approaches and communicate results and model limitations clearly to non-technical audiences.
• Document modelling assumptions, and validation results to support governance and audit requirements.
• Stay current with developments in time series and ML forecasting methods and recommend improvements to existing modelling practices.
• Support the wider ML team with utilizing the model outputs in business-oriented user interfaces.

What You'll Bring:

• 4+ years of experience in a data science, quantitative analytics, or applied statistics role, ideally with exposure to financial
• Hands-on experience building and tuning ARIMA (or similar classical time series) models and XGBoost (or comparable gradient-boosting) models.
• Working knowledge of Microsoft Fabric
• Proficiency in Python (pandas, stats models, scikit-learn, xgboost) and/or PySpark, solid SQL skills.
• Understanding of core statistical concepts: stationarity, seasonality, autocorrelation, cross-validation for time series, and bias-variance trade-offs.
• Strong communication skills, with the ability to explain modelling choices and forecast uncertainty to non-technical stakeholders.
• Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Computer Science, or a related quantitative field.

Nice to Have:

• Familiarity with MLOps practices (model versioning, CI/CD for ML, monitoring for drift) within Fabric or Azure ML.
• Exposure to actuarial, insurance, or macroeconomic forecasting contexts.