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Analytics Consultant Chennai

 

Data Engineer specializing in Machine Learning is responsible for designing, building, and maintaining the data infrastructure that supports machine learning initiatives. The Data Engineer will develop and optimize data models, implement data integration processes, and ensure data quality and accessibility for analytics and machine learning applications. The ideal candidate will have a strong background in data engineering, experience with ML frameworks, and a passion for transforming data into actionable insights.

 

To qualify for the role, you must have

Bachelor’s degree in business, Proven experience as a Data Engineer in a software development environment. Strong understanding of Data Integration Excellent communication, facilitation, and coaching skills. Ability to work collaboratively in a fast-paced environment. Proficient in Python and Machine Learning

 

Ideally you will also have:

Python Development:

Develop, test, and maintain high-quality Python code Utilize advanced Python features, including classes and object-oriented programming, to create efficient and reusable code. Leverage data manipulation libraries such as Pandas and NumPy to process and analyze large datasets effectively. Machine Learning Integration: Collaborate with cross-functional teams to integrate ML models into existing systems and applications. Apply knowledge of basic ML algorithms, including Linear Regression, Logistic Regression, and Random Forest, to solve real-world problems. Identify key outcomes of ML models and communicate findings to stakeholders in a clear and concise manner.

 

Statistical Analysis:

Conduct statistical hypothesis testing to validate model results and ensure the reliability of conclusions drawn from data. Interpret the results of statistical analyses and present insights in a client-facing manner, ensuring that complex concepts are easily understood by non-technical audiences.

 

Cloud Technologies:

Utilize Azure Cloud services and Azure Databricks for data processing, model training, and deployment. Optimize cloud resources to enhance performance and reduce costs associated with ML projects.

 

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