Data Scientist

JD description-

• A minimum of 6-9 years of relevant experience in data science, with a strong portfolio showcasing your expertise in machine learning, deep learning, GANs, synthetic data algorithms.
• Advanced proficiency in AWS cloud services (e. g., S3 EC2 Lambda, SageMaker, EKS) and a solid understanding of cloud architecture.
• Expert-level knowledge in Python and its data science libraries (TensorFlow, PyTorch, Scikit-learn, Pandas).
• Experience in building and optimizing data pipelines, architectures, and data sets, particularly in a real-time, SaaS environment.
• Design, develop, and deploy data pipelines and ETL processes
• Implement data integration solutions, ensuring data flows efficiently and reliably between various data sources and destinations.
• Collaborate with data architects and analysts to understand data requirements and translate them into technical specifications.
• Build and maintain scalable and optimized data storage solutions
• Develop and manage data transformation and cleansing processes to ensure data quality and accuracy.
• Monitor and troubleshoot data pipelines to identify and resolve issues in a timely manner.
• Optimize data pipelines for performance, cost, and scalability
• Exceptional analytical, problem-solving, and project management skills.
• Good communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders.
• A self-starter attitude with the ability to work in a fast-paced and startup environment.

Critical Skills to Possess:

• Proven experience as a Data Engineer, with a strong focus on cloud technologies.
• Solid understanding of data modeling, database design principles, and data warehousing concepts.
• Experience with data integration, ETL processes, and data transformation using tools like Azure Data Factory, Azure Logic Apps, or similar technologies.
• Strong programming skills in languages such as SQL, Python, or Spark.
• Familiarity with version control systems (e.g., Git) and CI/CD pipelines for code deployment.
• Knowledge of data security and compliance practices

Design & Develop ML Models

• Architect scalable machine learning solutions for complex problems.
• Select appropriate algorithms and frameworks based on use case.

Data Engineering & Feature Design

• Collaborate with data engineers to build robust data pipelines.
• Engineer features that improve model accuracy and performance.

Model Training & Optimization

• Train models on large datasets using distributed computing when needed.
• Optimize models for speed, accuracy, and resource efficiency.

Deployment & MLOps

• Deploy models into production using CI/CD pipelines.
• Monitor model performance and implement retraining strategies.

AI Infrastructure & Tooling

• Build and maintain infrastructure for experimentation and deployment.
• Evaluate and integrate new tools, libraries, and platforms.

Data Collection & Cleaning

• Gather data from various sources (databases, APIs, web scraping, etc.)
• Clean and preprocess data to ensure quality and consistency.

Exploratory Data Analysis (EDA)

• Analyze datasets to uncover patterns, trends, and insights.
• Use statistical techniques and visualization tools to summarize data.

Model Development

• Build predictive models using machine learning algorithms.
• Select appropriate models based on business needs and data characteristics.