Must have

• Python

• Data analysis

• Kedro

• Time Series Forecasting

• Anomaly Detection

• NLP

• Generative AI

• Deep learning

• AWS

• English

Nice to have

• Docker

• CI/CD

• Snowflake

Requirements description

Qualifications & Requirements:

• Substantial hands-on experience applying statistical modeling and ML to complex business problems.
• Expertise in exploratory and confirmatory data analysis, statistics, probability, and optimization.
• Proficiency in Python; experience with Kedro is highly desirable.
• Applied experience with Time Series Forecasting, Anomaly Detection, NLP, Generative AI, and Deep Learning.
• Proven experience with cloud infrastructure and services (AWS).
• Strong understanding of MLOps and DevOps principles.
• Containerization experience (Docker) and cloud-native Kubernetes services.
• Proficiency in CI/CD, automation, and infrastructure as code.
• Experience designing and developing data warehouses (e.g., Snowflake).
• Strong SQL development skills and ability to build end-to-end data pipelines.
• Expertise in solution architecture design.

Offer description

Project Description:

Join a dynamic, cutting-edge project in the [finance/pharma/technology] sector focused on leveraging advanced data science, machine learning, and cloud-native solutions. The project aims to implement scalable AI/ML pipelines, optimize data-driven decision-making, and deliver actionable insights through state-of-the-art data engineering and MLOps practices. You will work closely with cross-functional teams to design, develop, and deploy AI solutions that have a direct impact on business outcomes.

Your responsibilities

1. Apply statistical modeling and machine learning techniques to solve complex business challenges.
2. Conduct exploratory and confirmatory data analysis, ensuring high-quality data preprocessing and preparation.
3. Develop and implement Time Series Forecasting, Anomaly Detection, NLP, Generative AI, and Deep Learning solutions.
4. Design, build, and maintain end-to-end data pipelines and data warehousing solutions.
5. Implement MLOps/DevOps best practices, including CI/CD pipelines, containerization (Docker), and orchestration with cloud-native Kubernetes services (e.g., Amazon EKS, GKE, AKS).
6. Automate infrastructure using Terraform, AWS CloudFormation, or AWS CDK.

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