Data & AI Sales Specialist

A Data & AI Specialist is responsible for designing, developing, and implementing data-driven and AI-powered solutions to improve business processes, optimize performance, and enable data-informed decision-making. The role involves managing large datasets, applying advanced analytics and machine learning techniques, and collaborating across teams to integrate AI technologies into business operations.

Key Responsibilities:

• Collect, clean, and preprocess structured and unstructured data from multiple sources.

• Design and develop machine learning models and AI algorithms for predictive and prescriptive analytics.

• Build data pipelines and automate data workflows for continuous data integration and model deployment.

• Analyze business requirements and identify opportunities to apply AI and data analytics for improvement.

• Collaborate with data engineers, developers, and business stakeholders to implement data-driven solutions.

• Monitor model performance, perform tuning, and ensure scalability and reliability.

• Create dashboards, reports, and visualizations to communicate insights clearly to non-technical audiences.

• Stay current with emerging AI technologies, frameworks, and industry best practices.

Required Skills and Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.

• Proficiency in Python, R, or SQL for data analysis and model development.

• Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).

• Familiarity with cloud platforms (AWS, Azure, GCP) and data tools (Databricks, Snowflake, Power BI, etc.).

• Strong understanding of data modeling, statistics, and algorithm design.

• Excellent problem-solving, analytical thinking, and communication skills.

Preferred Qualifications:

• Experience in Natural Language Processing (NLP), Computer Vision, or Generative AI.

• Knowledge of MLOps and model deployment pipelines.

• Hands-on experience with big data technologies (Spark, Hadoop, Kafka).