Data Scientist

Responsibilities

• Model Development & Implementation:
  Design, build, and deploy machine learning models to solve complex business problems. These may include predictive analytics, recommendation systems, anomaly detection, classification, and other machine learning tasks.
• AI Solutions:
  Create and optimize AI-driven solutions that can automate processes, improve decision-making, and enhance user experiences across various business functions.
• Data Analysis & Exploration:
  Conduct thorough analysis of large datasets to uncover patterns, trends, and insights that inform business strategies and model development.
• Collaborative Problem Solving:
  Partner with business stakeholders to understand their goals and challenges, and translate these into data-driven solutions and models that align with the organization’s objectives.
• Model Optimization:
  Continuously monitor, fine-tune, and improve the performance of machine learning models through evaluation, experimentation, and iteration.
• Scalability & Performance:
  Ensure that AI models and solutions can scale effectively in production environments, handling large volumes of data while maintaining high performance and reliability.
• Stay Current with AI Trends:
  Keep up-to-date with the latest advancements in AI, machine learning, and data science to propose innovative techniques and technologies that can drive competitive advantage.
• Reporting & Communication:
  Present findings, model results, and actionable insights to both technical and non-technical stakeholders, ensuring clarity and alignment on business impacts.

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
• Proven experience (5+ years) as a Data Scientist, with a focus on machine learning, AI, and advanced analytics.
• Strong communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
• Strong proficiency in programming languages such as Python, R, or similar.
• Hands-on experience with machine learning frameworks (e.g., Tensor Flow, PyTorch, scikit-learn, Keras) and tools for model development, training, and evaluation.
• Expertise in data manipulation and analysis using tools like Pandas, Num Py, SQL, or similar.
• Solid understanding of statistical methods, hypothesis testing, and data modeling techniques.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and deploying models in cloud environments is a plus.