Hybrid Data Scientist - AI/ML Solutions for Real Impact

Digica

Stretford

Hybrid

GBP 60,000 - 90,000

Full time

14 days+

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Benefits offered by this job

Hybrid work model

Job summary

Digica is seeking a passionate Data Scientist to solve real business problems with data and machine learning. You’ll design and deliver ML solutions end-to-end—from exploration and experimentation to deployment—and work with diverse data and cross-functional teams.

You’ll build CV/ML/AI models, select and optimize algorithms, integrate models into production, and translate insights for clients and internal stakeholders.

Qualifications

  • 3+ years of experience in data analysis, computer vision, machine learning, or statistical modeling.
  • Practical experience with ML model deployment (cloud or on-premise).
  • Strong Python programming skills (NumPy, pandas, scikit-learn, PyTorch or TensorFlow).
  • Understanding of Windows or Linux software development platforms
  • Good understanding of statistics and data preparation
  • Working knowledge of SQL and version control tools (Git)
  • Fluent English – for international communication and collaboration.

Responsibilities

  • Designing and developing CV/ML/AI models (predictive, classification, recommendation, LLM-based).
  • Selecting and optimizing algorithms (regression, tree-based, neural networks, etc.).
  • Integrating models into production environments.
  • Performing data cleaning, feature engineering, and model validation.
  • Collaborating with clients and internal teams to translate analytical results into actionable insights.

Skills

Python
SQL
Git
ML deployment

Tools

Docker
MLflow
Airflow
AWS/GCP/Azure

Job description

Digica is seeking a passionate Data Scientist to solve real business problems with data and machine learning. You’ll design and deliver ML solutions end-to-end—from exploration and experimentation to deployment—and work with diverse data and cross-functional teams.

You’ll build CV/ML/AI models, select and optimize algorithms, integrate models into production, and translate insights for clients and internal stakeholders.

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