Assistant Manager

About The Role

• Analyse large datasets using Python to surface actionable insights
• Build, evaluate, and maintain machine learning models for classification, regression, and NLP tasks
• Integrate and work with generative AI, including local and cloud-hosted LLMs, to augment analytical workflows and automate text-heavy tasks
• Design and maintain interactive dashboards and reports in Power BI
• Write and optimise SQL queries for data extraction, transformation, and reporting
• Prepare executive-ready presentations and summaries in PowerPoint; maintain structured data in Excel
• Apply statistical methods (e.g. hypothesis testing, regression analysis, distribution modelling) to validate findings
• Collaborate with cross-functional stakeholders to translate business questions into analytical frameworks
  

Required Skills & Qualifications

• Python: pandas, scikit-learn, seaborn, NLTK
• Data visualisation: Power BI (report building, DAX, data modelling)
• Databases: SQL (query writing, joins, aggregations, window functions)
• Office tools: Microsoft Excel (PivotTables and Power Query) and PowerPoint
• Statistical acumen: hypothesis testing, A/B testing, regression, probability distributions, significance testing
  

• Bachelor’s or master’s degree in Statistics, Mathematics, Data Science, Analytics, Computer Science, Engineering, or a related quantitative field
• Formal education requirements may be waived for candidates who demonstrate equivalent competence through work experience and a strong portfolio
  

Experience

• 1–2 years of hands-on work experience in a data analyst, business analyst, or equivalent role
• Demonstrated experience working with real-world datasets and delivering analytical outputs
  

• Familiarity with NLP pipelines and text analytics
• Experience with big data processing using PySpark
• Linux CLI proficiency: navigating file systems, shell scripting, running jobs on remote servers
• ML deployment exposure: serving models via FastAPI and building lightweight interfaces using Streamlit or Gradio
• Knowledge of data warehousing concepts or cloud platforms (Azure, GCP, AWS)