Test Yantra
Data Scientist
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Scientist with a contract length of "unknown", offering a pay rate of "unknown". Key skills include Python, SQL, machine learning model development, and deployment. Experience in financial time-series analysis is essential.
🌎 - Country
United Kingdom
💱 - Currency
£ GBP
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💰 - Day rate
Unknown
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🗓️ - Date
June 19, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
London Area, United Kingdom
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🧠 - Skills detailed
#ML (Machine Learning) #Supervised Learning #Data Science #Deployment #Python #SageMaker #Unsupervised Learning #AI (Artificial Intelligence) #NumPy #Data Analysis #Version Control #GIT #Data Wrangling #Pandas #Classification
Role description
Responsibilities:
• Design and develop AI / ML based solution
• sWork with other data scientists to build and deploy production-level solution
• sTroubleshoot and debug cod
• eWork with other teams to understand and solve business problem
s
Essential Skill
• s:
Python (pandas, NumPy, scikit-learn): For data wrangling, modelling, and feature engineer
• ingSQL: For querying structured data sour
• cesModel Development & Validation: Experience with classification, unsupervised learning (e.g. outlier detection), and ranking mod
• elsMachine Learning Deployment: Familiarity with containerised deployment (e.g. Podman, SageMaker, DSW pipelin
• es)Version Control (Git): To maintain reproducible and collaborative workfl
• owsTime-Series Analysis: To assess risk trends over financial ye
• arsExploratory Data Analysis (EDA): To spot early signals or risk clust
ers
Responsibilities:
• Design and develop AI / ML based solution
• sWork with other data scientists to build and deploy production-level solution
• sTroubleshoot and debug cod
• eWork with other teams to understand and solve business problem
s
Essential Skill
• s:
Python (pandas, NumPy, scikit-learn): For data wrangling, modelling, and feature engineer
• ingSQL: For querying structured data sour
• cesModel Development & Validation: Experience with classification, unsupervised learning (e.g. outlier detection), and ranking mod
• elsMachine Learning Deployment: Familiarity with containerised deployment (e.g. Podman, SageMaker, DSW pipelin
• es)Version Control (Git): To maintain reproducible and collaborative workfl
• owsTime-Series Analysis: To assess risk trends over financial ye
• arsExploratory Data Analysis (EDA): To spot early signals or risk clust
ers
