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# Big Data Developer with Python

- **Company:** Information Tech Consultants
- **Location:** Greater London, UK
- **Experience:** Starter
- **Salary:** £35,000.0 - £45,000.0
- **Contract:** Permanent contract
- **Skills:** Computer Vision, Big Data, Computer Programming, Data Integrity, Relational Databases, Apache Hadoop, Python (Programming Language), NoSQL, NumPy, Open Source Technology, Power BI, Tensorflow, SQL Databases, Tableau (Software), Reinforcement Learning, Pytorch, Apache Spark, Keras, Pandas, Matplotlib, Containerization, Scikit Learn, Kubernetes, Information Technology, Apache Kafka, Data Pipelines, Docker
- **Published:** June 18, 2026
- **Apply:** https://uk.indeed.com/viewjob?jk=5a04c059275e5b80

## About the Role

Do you have experience in SQL?, Do you have a Bachelor's degree?, * Strong programming skills in Python with knowledge in ML/Al libraries (TensorFlow, PyTorch, scikit-learn, Keras).

 * Experience with SQL/NoSQL databases and data pipelines.
 * Familiarity with big data technologies like Hadoop, Spark, or Kafka

Preferred Skill Sets:

 * Good to have large datasets and using tools such as Pandas, NumPy, and Matplotlib.
 * Good to have knowledge with containerization and orchestration (e.g., Docker, Kubernetes).
 * Contributions to open-source ML/Al projects.
 * Knowledge in Natural Language Processing (NLP), computer vision, or reinforcement learning.
 * Excellent problem-solving and critical thinking abilities.
 * Ability to work independently and collaboratively within a team.
 * Strong communication skills to explain technical details to non-technical stakeholders.

Education: Master's degree in data science, Statistics, Computer Science, Mathematics, or a related quantitative field (or equivalent bootcamp experience).

Strong mathematics and statistics background., * Bachelor's (preferred)

## Description

* Model Building: Assist in developing and testing machine learning models (Regression, Classification, etc.) using Python.
 * Database Management: Write and optimize SQL queries to extract data from various relational databases.
 * Visualization: Create clear and compelling dashboards (Tableau, Power BI, or Matplotlib) to communicate findings to stakeholders.
 * Collaboration: Partner with engineering and product teams to understand data needs and ensure data integrity.
 * Continuous Learning: Stay up-to-date with the latest data science trends and participate in internal knowledge-sharing sessions.


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