Data Science Analyst (Python)

• Manage data pipelines and support data collection, cleaning, validation, and preparation from multiple data sources.
• Conduct exploratory data analysis to identify trends, patterns, risks, and opportunities.
• Build, evaluate, and improve predictive and machine learning models (including regression, classification, clustering, time-series, neural networks, NLP/LLM, and recommendation systems).
• Develop reusable Python-based analytical workflows and support automation of data science processes.
• Support deployment of AI, analytics, or automation solutions into client or operational environments.
• Produce dashboards, reports, and presentations to communicate technical findings to business stakeholders.
• Assist in gathering, documenting, and translating business requirements for AI and analytics initiatives.
• Participate in process mapping, workflow analysis, and requirements workshops where appropriate.
• Help evaluate opportunities where AI or automation may improve existing business processes.
• Support testing, implementation, and adoption activities for AI solutions while applying structured problem-solving techniques.

Do you have 2 to 3 years of industry project experience in Data Science, Machine Learning, Analytics, or a related quantitative field?

Our client is seeking a passionate Data Science Analyst to manage the end-to-end data science lifecycle using Python and SQL, while transforming complex challenges into cutting-edge AI solutions. You will thrive here if you bring exposure to business analysis, process improvement, and BI platforms like Power BI or Tableau. Experience working in Agile/Scrum environments with tools like Jira or Confluence-especially within consulting, financial services, or marketing-will make you an ideal fit!, * Approximately 2 to 3 years of industry project experience in Data Science, Machine Learning, Analytics, or a related quantitative field.

• Strong Python skills with experience using libraries such as Pandas, NumPy, TensorFlow, or similar.
• Experience with data preparation, exploratory data analysis, statistical modelling, and machine learning.
• Ability to communicate technical concepts clearly to both technical and non-technical audiences.
• Familiarity with the end-to-end data science lifecycle.
• Strong analytical thinking and structured problem-solving skills.
• Good project management, stakeholder communication, and collaboration skills.

Nice to have:

• Exposure to business analysis, requirements gathering, or process improvement initiatives.
• Familiarity with Agile project delivery environments, Scrum, Lean, or Lean Six Sigma methodologies.
• Experience using Jira, Confluence, Microsoft DevOps, or similar project management tools.
• Experience with SQL, Power BI, Tableau, or other BI platforms.
• Experience working within consulting, professional services, financial services, or marketing functions.

Assessment Period:

Candidates who successfully pass the initial and final interview stages will proceed to a one-month part-time assessment period with the client. During this period, their performance and overall fit will be evaluated. Candidates who complete the assessment will be offered a full-time position.