Senior Data Scientist

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1. Data Collection & Cleaning: Collect, preprocess, and ensure data quality across large datasets, implementing best practices in data cleaning and integrity management.
2. Feature Engineering & Data Transformation: Design and extract relevant features from raw data to optimize machine learning model performance and ensure the best data representation.
3. Data Analysis & Predictive Modeling: Utilize statistical and machine learning techniques to identify trends, patterns, and correlations in data, and build predictive models for forecasting and decision support.
4. Hypothesis Testing & Insights Generation: Formulate and test hypotheses to derive actionable insights, providing data-driven recommendations for business strategies.
5. Data Visualization & Reporting: Develop intuitive data visualizations, dashboards, and reports using tools like Matplotlib, Seaborn, Plotly, Power BI, or Tableau to communicate findings effectively to both technical and non-technical stakeholders.
6. Generative AI Model Development: Train and fine-tune generative AI models using state-of-the-art techniques such as LoRA/QLoRA, and optimize these models for real-world applications.
7. Agentic AI & Retrieval-Augmented Generation (RAG): Design, develop, optimize, and deploy Agentic AI systems and RAG models to facilitate intelligent information retrieval and generate contextually relevant outputs.
8. Vector Database Management & Information Retrieval: Leverage vector databases and advanced indexing techniques to enable efficient storage and retrieval of relevant data, particularly for conversational contexts.
9. API & Microservices Development: Build and maintain scalable APIs and microservices to facilitate AI model integration into applications and enterprise solutions.
10. Model Deployment & Optimization: Deploy machine learning models into production environments, ensuring seamless integration with existing pipelines and real-time processing capabilities.
11. MLOps & Automation: Implement and manage MLOps best practices, including CI/CD pipelines, model versioning, monitoring, and automated retraining to maintain performance and reliability.
12. Collaboration & Decision Support: Work closely with cross-functional teams, including product managers, engineers, and stakeholders, to deliver data-driven insights and support decision-making processes.
13. Data Privacy & Security: Ensure all data handling practices comply with relevant privacy and security regulations, protecting both sensitive data and intellectual property.
14. Continuous Learning & Innovation: Stay current with the latest advancements in AI and data science, applying new methodologies and tools to enhance model performance and business outcomes.

Requirements:
Technical Skills:

• Strong proficiency in statistical analyses and hypothesis testing.
• Expertise in machine learning algorithms and techniques, including supervised and unsupervised learning.
• Hands-on experience with data manipulation and cleaning using Python, along with libraries like Pandas and NumPy.
• Proficient in creating data visualizations using tools such as Matplotlib, Seaborn, or Plotly. Deep experience in programming, particularly in Python, for data science applications.
• Familiarity with database systems (SQL, NoSQL) and experience in data retrieval and storage.
• Practical knowledge of big data tools like Hadoop and Spark.
• Ability to develop and integrate APIs for AI model consumption within enterprise applications.
• Experience in model performance monitoring, debugging, and optimization in real-world use cases.

Generative AI Expertise:

• Experience with training, fine-tuning, and deploying generative AI models (e.g., GANs, VAEs, transformers).
• Proficiency in designing and optimizing Agentic AI systems and RAG models.
• Experience with vector databases and retrieval-based AI models for conversational AI and information retrieval.

Cloud and Deployment Expertise:

• Hands-on experience with large-scale model training and fine-tuning on cloud platforms like AWS, GCP, or Azure.
• Familiarity with MLOps tools for model versioning, monitoring, and deployment.

Problem-Solving & Domain Expertise:

• Strong problem-solving skills and the ability to think critically and innovate.
• Industry-specific domain knowledge is a plus (e.g., healthcare, finance, e-commerce).
• Expertise in deploying models in serverless architectures or containerized environments.

Education & Experience:

• A Bachelor's or master's degree in data science, Computer Science, Statistics, or a related field.
• Minimum 4 years of experience in data science or machine learning, including at least 6 months to 1 year of hands-on experience with generative AI projects.
• Relevant certifications (e.g., Certified Data Scientist, Certified Analytics Professional) are beneficial.

Seniority level

• Mid-Senior level

Employment type

• Full-time

Job function

• Information Technology

Industries

• IT Services and IT Consulting

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