Data Scientist

Required Technical Skills and Qualifications

• 4+ years of experience in data science, machine learning, statistical modeling, or advanced analytics.
• Strong hands-on experience with Python for data analysis, machine learning, automation, and AI application development.
• Strong proficiency in SQL, including complex queries, data extraction, transformation, and analysis.
• Hands-on experience with Apache Spark and PySpark for distributed data processing and large-scale data analytics.
• Experience developing generative AI solutions and intelligent automation workflows to improve business processes and operational efficiency.
• Practical experience building AI-powered applications using LLMs, including prompt engineering and integration of AI capabilities into business applications.
• Familiarity with RESTful API design, development, and integration to connect AI solutions with enterprise systems.
• Understanding of containerization and application deployment technologies, such as Docker and Kubernetes.
• Familiarity with modern front-end frameworks, such as React or Angular, and the development of user-facing applications.
• Understanding of deploying, testing, monitoring, and optimizing AI/ML models and applications in production environments.

Key Responsibilities

• Analyze large and complex datasets to identify trends, patterns, and actionable business insights.
• Develop, implement, and optimize machine learning models and advanced analytical solutions.
• Build scalable data processing and analytics pipelines using Python, SQL, Apache Spark, and PySpark.
• Design and develop AI-powered applications using generative AI technologies and large language models.
• Implement intelligent automation solutions to streamline business workflows and improve operational efficiency.
• Integrate LLMs and AI/ML capabilities into applications using APIs and related frameworks.
• Collaborate with engineering, product, and business teams to translate requirements into scalable data science and AI solutions.
• Support application deployment, testing, troubleshooting, and performance optimization.
• Contribute to the development of user-facing applications that incorporate AI-driven capabilities.
• Follow best practices for code quality, data security, scalability, and responsible AI development.

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