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Senior Data Scientist – Conventional AI & Generative AI Specialist

Min Experience

4 years

Location

Karachi

JobType

full-time

About the job

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About the role

Data Collection & Cleaning: Collect, preprocess, and ensure data quality across large datasets, implementing best practices in data cleaning and integrity management. Feature Engineering & Data Transformation: Design and extract relevant features from raw data to optimize machine learning model performance and ensure the best data representation. 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. · Hypothesis Testing & Insights Generation: Formulate and test hypotheses to derive actionable insights, providing data-driven recommendations for business strategies. 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. 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. 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. 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. API & Microservices Development: Build and maintain scalable APIs and microservices to facilitate AI model integration into applications and enterprise solutions. Model Deployment & Optimization: Deploy machine learning models into production environments, ensuring seamless integration with existing pipelines and real-time processing capabilities. MLOps & Automation: Implement and manage MLOps best practices, including CI/CD pipelines, model versioning, monitoring, and automated retraining to maintain performance and reliability. 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. Data Privacy & Security: Ensure all data handling practices comply with relevant privacy and security regulations, protecting both sensitive data and intellectual property. 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.

Skills

data collection
data cleaning
data transformation
feature engineering
predictive modeling
hypothesis testing
data visualization
generative ai
agentic ai
rag
vector database
information retrieval
api development
microservices
model deployment
mlops
cloud
problem solving