Data Scientist & Analytics

ABB

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1 day ago

Key Responsibilities

In this role, you will deliver and implement analytics strategies while collaborating closely with business process owners, domain experts, and digital transformation leaders. Main responsibilities include:
Use Case Implementation: Identify and implement analytics use cases encompassing data collection, preparation, feature engineering, testing, and deployment.
Model Development: Build and enhance large-scale AI/ML models and design data pipelines to analyze and visualize complex business data.
Data Analytics Lifecycle: Apply the complete data lifecycle—from initial discovery, modeling, and validation to production deployment and monitoring.
Cross-Functional Collaboration: Work alongside diverse teams to solve business challenges through data-driven methodologies and provide actionable insights.

A Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, Statistics, Mathematics, or a related quantitative field.
0–2 years of experience in Data Science, Data Analytics, or Machine Learning (academic projects, internships, and research assignments are fully counted).
Strong working knowledge of Python , SQL , and Snowflake .
High proficiency in Python data science libraries including Pandas, NumPy, and Scikit-learn.
Understanding of data modeling, ETL/ELT concepts, APIs, and software development best practices (e.g., Git version control).
Experience utilizing Azure cloud services and data visualization platforms like Power BI.
Passionate about problem-solving, analytical thinking, and continuous learning.
Excellent ability to communicate technical concepts to non-technical audiences clearly.
Fluent in written and spoken English.
Passionate about problem-solving, analytical thinking, and continuous learning.
Excellent ability to communicate technical concepts to non-technical audiences clearly.
Fluent in written and spoken English.
Applicants should be proficient in Python, SQL, Snowflake Data Cloud, machine learning libraries (Pandas, NumPy, Scikit-learn), and tools like Power BI and Azure.
Candidates must hold a Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Software Engineering, Statistics, Mathematics, or a similar quantitative discipline.

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