Data Scientist

Responsibilities

• A Data Scientist with 5 to 10 years of experience is responsible for leveraging data to uncover insights, create predictive models, and drive data‑driven decision‑making within an organization.
• This role involves advanced analytics, machine learning, and strong problem‑solving skills to extract actionable information from large datasets.
• Data Analysis:
  Collect, clean, and analyze complex datasets to identify trends, patterns, and actionable insights.
• Use statistical techniques to uncover meaningful information from data.
• Predictive Modeling:
  Develop and deploy machine learning models to predict future trends, behaviors, and outcomes. Apply regression analysis, clustering, classification, and other modelling techniques.
• Data Visualization:
  Create compelling visualisations to communicate findings to both technical and non‑technical stakeholders using tools like Tableau, Power BI, or Python libraries.
• Hypothesis Testing:
  Formulate and test hypotheses, providing statistical validation for business decisions and recommendations.
• Feature Engineering:
  Engineer and select relevant features for machine learning models, enhancing their predictive power.
• Algorithm Development:
  Build and fine‑tune machine learning algorithms such as decision trees, random forests, neural networks, and more, depending on the problem.
• Data Integration:
  Collaborate with IT and database administrators to integrate and access data from various sources and data warehouses.
• Model Deployment:
  Deploy machine learning models in production environments to support real‑time decision‑making.
• A/B Testing:
  Design and analyse A/B tests to measure the impact of changes and improvements.
• Data Ethics:
  Ensure ethical data practices, including privacy and compliance with data protection regulations.
• Cross‑functional
  
  Collaboration:
  
  Collaborate with engineers, business analysts and domain experts to understand business requirements and align data science initiatives with organisational goals.
• Mentorship:
  Provide guidance and mentorship to junior data scientists and analysts, fostering their professional growth.
• Continuous Learning:
  Stay updated on the latest data science tools, techniques, and trends through ongoing professional development.

• Bachelor’s degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Engineering); a Master’s or Ph.D. is a plus.
• 5 to 10 years’ experience in data science, including machine learning and statistical analysis.
• Proficiency in tools and programming languages such as Python, R, or Julia.
• Strong knowledge of machine learning algorithms and their applications.
• Experience with data visualization tools like Tableau, Power BI, or Python libraries such as Matplotlib and Seaborn.
• Solid understanding of databases and data manipulation using SQL.
• Excellent problem‑solving and critical‑thinking skills.
• Strong communication skills to convey complex findings to both technical and non‑technical stakeholders.
• Familiarity with big data technologies and distributed computing frameworks (e.g., Hadoop, Spark) is a plus.
• Knowledge of data ethics, privacy and compliance considerations.