3+ Data manipulation Jobs in Pune | Data manipulation Job openings in Pune

JOB DETAILS:

- Job Title: Lead I - Data Science - Python, Machine Learning, Spark 

- Industry: Global Digital Transformation Solutions Provider

- Experience: 5-10 years

- Job Location: Pune

- CTC Range: Best in Industry

JD for Data Scientist

Hands-on experience with data analysis tools:

Proficient in using tools such as Python and R for data manipulation, querying, and analysis.

Skilled in utilizing libraries like Pandas, NumPy, and Scikit-Learn to perform in-depth data analysis and modeling.

Skilled in machine learning and predictive analytics:

Expertise in building, training, and deploying machine learning models using frameworks such as TensorFlow and PyTorch.

Capable of performing tasks like regression, classification, clustering, and recommendation, leading to data-driven predictions and insights.

Expertise in big data technologies:

Proficient in handling large datasets using big data tools such as Spark.

Skilled in employing distributed computing and parallel processing techniques to ensure efficient data processing, storage, and analysis, enabling enterprise-level solutions and informed decision-making

Skills: Python, SQL, Machine Learning, and Deep Learning, with mandatory expertise in Generative AI.

Must-Haves

5–9 years of relevant experience in Python, SQL, Machine Learning, and Deep Learning, with mandatory expertise in Generative AI

******

NP - Immediate joiners only

Location-Pune 

Core Responsibilities:

• The MLE will design, build, test, and deploy scalable machine learning systems, optimizing model accuracy and efficiency
• Model Development: Algorithms and architectures span traditional statistical methods to deep learning along with employing LLMs in modern frameworks.
• Data Preparation: Prepare, cleanse, and transform data for model training and evaluation.
• Algorithm Implementation: Implement and optimize machine learning algorithms and statistical models.
• System Integration: Integrate models into existing systems and workflows.
• Model Deployment: Deploy models to production environments and monitor performance.
• Collaboration: Work closely with data scientists, software engineers, and other stakeholders.
• Continuous Improvement: Identify areas for improvement in model performance and systems.

Skills:

• Programming and Software Engineering: Knowledge of software engineering best practices (version control, testing, CI/CD).
• Data Engineering: Ability to handle data pipelines, data cleaning, and feature engineering. Proficiency in SQL for data manipulation + Kafka, Chaossearch logs, etc for troubleshooting; Other tech touch points are ScyllaDB (like BigTable), OpenSearch, Neo4J graph
• Model Deployment and Monitoring: MLOps Experience in deploying ML models to production environments.
• Knowledge of model monitoring and performance evaluation.

Required experience:

• Amazon SageMaker: Deep understanding of SageMaker's capabilities for building, training, and deploying ML models; understanding of the Sagemaker pipeline with ability to analyze gaps and recommend/implement improvements
• AWS Cloud Infrastructure: Familiarity with S3, EC2, Lambda and using these services in ML workflows
• AWS data: Redshift, Glue
• Containerization and Orchestration: Understanding of Docker and Kubernetes, and their implementation within AWS (EKS, ECS)

Skills: Aws, Aws Cloud, Amazon Redshift, Eks

Must-Haves

Amazon SageMaker, AWS Cloud Infrastructure (S3, EC2, Lambda), Docker and Kubernetes (EKS, ECS), SQL, AWS data (Redshift, Glue)

Skills : Machine Learning, MLOps, AWS Cloud, Redshift OR Glue, Kubernetes, Sage maker

******

Notice period - 0 to 15 days only 

Location : Pune & Hyderabad only

MUST-HAVES: 

• Machine Learning + Aws + (EKS OR ECS OR Kubernetes) + (Redshift AND Glue) + Sage maker
• Notice period - 0 to 15 days only 
• Hybrid work mode- 3 days office, 2 days at home

SKILLS: AWS, AWS CLOUD, AMAZON REDSHIFT, EKS

ADDITIONAL GUIDELINES:

• Interview process: - 2 Technical round + 1 Client round
• 3 days in office, Hybrid model. 

CORE RESPONSIBILITIES:

• The MLE will design, build, test, and deploy scalable machine learning systems, optimizing model accuracy and efficiency
• Model Development: Algorithms and architectures span traditional statistical methods to deep learning along with employing LLMs in modern frameworks.
• Data Preparation: Prepare, cleanse, and transform data for model training and evaluation.
• Algorithm Implementation: Implement and optimize machine learning algorithms and statistical models.
• System Integration: Integrate models into existing systems and workflows.
• Model Deployment: Deploy models to production environments and monitor performance.
• Collaboration: Work closely with data scientists, software engineers, and other stakeholders.
• Continuous Improvement: Identify areas for improvement in model performance and systems.

SKILLS:

• Programming and Software Engineering: Knowledge of software engineering best practices (version control, testing, CI/CD).
• Data Engineering: Ability to handle data pipelines, data cleaning, and feature engineering. Proficiency in SQL for data manipulation + Kafka, Chaos search logs, etc. for troubleshooting; Other tech touch points are Scylla DB (like BigTable), OpenSearch, Neo4J graph
• Model Deployment and Monitoring: MLOps Experience in deploying ML models to production environments.
• Knowledge of model monitoring and performance evaluation.

REQUIRED EXPERIENCE:

• Amazon SageMaker: Deep understanding of SageMaker's capabilities for building, training, and deploying ML models; understanding of the Sage maker pipeline with ability to analyze gaps and recommend/implement improvements
• AWS Cloud Infrastructure: Familiarity with S3, EC2, Lambda and using these services in ML workflows
• AWS data: Redshift, Glue
• Containerization and Orchestration: Understanding of Docker and Kubernetes, and their implementation within AWS (EKS, ECS)