Data Scientist

Responsibilities:

• Data Collection and Cleaning: Collect and pre-process large datasets, ensuring data quality and integrity.
• Feature Engineering: Identify and engineer relevant features for model development and performance improvement.
• Data Analysis: Apply statistical and machine learning techniques to analyze data, identify trends, correlations, and patterns, and develop predictive
• models.
• Hypothesis Testing: Formulate and test hypotheses to answer specific business questions and guide decision-making.
• Data Visualization: Create data visualizations and dashboards to present complex data in an understandable and actionable manner.
• Predictive Modeling: Develop and implement machine learning models to make predictions and recommendations based on data.
• Data Interpretation: Translate data findings into actionable insights and strategic recommendations for stakeholders.
• Data-driven Decision Support: Collaborate with cross-functional teams to provide data-driven insights that inform business strategies and decisions.
• Data Privacy and Security: Ensure that data handling and analysis comply with data privacy and security regulations.
• Generative AI Model Training & Fine-tuning: Train/Fine-tune generative AI models using state-of-the-art training techniques such as LoRA/QLoRA.
• Agentic AI & Retrieval-Augmented Generation (RAG) Design: Design, develop, and optimize Agentic AI workflows and RAG (Retrieval-Augmented Generation) models to facilitate effective information retrieval and generation.
• Vector Database Management & Information Retrieval: Utilize vector databases and advanced indexing techniques to efficiently store and retrieve relevant information for conversational contexts.
• Continual Learning: Stay up-to-date with the latest data science techniques and tools and apply them to improve processes.
• Communication: Effectively communicate findings and insights to both technical and non-technical stakeholders through reports and presentations.

Requirements:

• Statistical Analysis: Proficiency in statistical analysis and hypothesis testing to draw meaningful conclusions from data.
• Machine Learning: Strong knowledge of machine learning techniques and algorithms, including supervised and unsupervised learning.
• Data Manipulation: Proficiency in data manipulation and cleaning using Python.
• Data Visualization: Skill in creating data visualizations using tools like Matplotlib, Seaborn, or Tableau.
• Programming: Strong programming skills, particularly in Python, for data analysis and model development.
• Database Knowledge: Familiarity with database systems and SQL for data retrieval and storage.
• Big Data Tools: Experience with big data technologies such as Hadoop and Spark.
• Feature Engineering: Ability to engineer relevant features from raw data.
• Domain Expertise: Understanding of the specific industry or domain in which the organization operates.
• Problem-Solving: Excellent problem-solving skills and the ability to think critically.
• Education and Certification: A bachelor's or master's degree in a relevant field (e.g., data science, statistics, computer science) is typically required.
• Relevant certifications, such as Certified Data Scientist (CDS) or Certified Analytics Professional (CAP), can be beneficial.
• Hands-on experience with large-scale model training and fine-tuning on cloud platforms (AWS, GCP, Azure)
• Familiarity with MLOps tools for model versioning, monitoring, and deployment.
• Experience building and deploying generative AI models.
• Experience: Minimum 3 years of experience in data science or machine learning, with at least 6 months to 1 year of recent hands-on experience in generative AI projects for clients.