Data Scientist AI

Synopsis

As a Data Scientist, you will lead the design, development, and deployment of advanced machine learning models and analytical solutions to support strategic decision-making and AI initiatives. You will collaborate with data engineers, product owners, and business stakeholders to extract insights from large datasets and translate them into actionable outcomes. This role requires strong analytical thinking, advanced statistical knowledge, and expertise in machine learning and data science best practices.

Accountabilities

• Design, develop, and deploy machine learning models and AI solutions for business-critical problems.
• Collaborate with data engineers to ensure data quality, integrity, and accessibility for modeling and analysis.
• Analyze complex datasets using statistical and predictive modeling techniques to uncover trends and patterns.

• Translate business problems into data science solutions, defining success metrics and model evaluation criteria.
• Mentor junior data scientists and guide best practices in feature engineering, model tuning, and validation.
• Present findings and model results to stakeholders through clear, data-driven storytelling and visualization.
• Stay updated on the latest advancements in machine learning, AI, and analytics technologies.
• Work within an agile team to deliver iterative, high-impact solutions in alignment with project timelines.
• Ensure models and analytics workflows are scalable, reproducible, and production-ready.
• Adhere to ethical AI and data governance standards throughout the development lifecycle.
  
  

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  Get Hired
  Bachelor s in Computer Science, Data Science, Mathematics, Statistics, or a related field.
• 5+ years of experience in data science or applied machine learning roles.
• Proficiency in Python or R, with strong experience in libraries such as scikit-learn, TensorFlow, PyTorch, or XGBoost.
• Solid understanding of statistical methods, machine learning algorithms, and data mining techniques.
• Experience with data visualization tools (e.g., Power BI, Tableau, Plotly, or matplotlib/seaborn).
• Familiarity with big data platforms (e.g., Apache Spark) and cloud-based environments (e.g., AWS, Azure, or GCP).
• Strong communication and stakeholder management skills.
• Experience with model deployment, monitoring, and lifecycle management is a plus.