Research Scientist- Remote/ India
Job Description
We are seeking a business-oriented Machine Learning Engineer with strong fundamentals in classical ML and modern AI, including Generative AI. This role focuses on applying AI to real-world business problems, particularly in time series, tabular, and document-based data.
The ideal candidate is comfortable working autonomously, collaborating across international teams, and translating complex analytical findings into clear, actionable insights for stakeholders.
Key Responsibilities
Analyze complex datasets (time series, tabular, numerical, and temporal data) to identify patterns, trends, anomalies, and outliers
Design, build, evaluate, and explain ML models aligned to real business use cases
Break down ambiguous or complex business problems into solvable ML tasks
Interpret model results, analyze errors, and connect outcomes back to business impact
Communicate findings clearly to non-technical stakeholders and ask the right follow-up questions
Propose and iterate on AI-driven solutions based on data insights
Collaborate closely with cross-functional and international teams in English
We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to [email protected] learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.
Skills and Requirements
Strong fundamentals in machine learning concepts
Hands-on experience with deep learning / neural networks and modern AI approaches
Solid understanding of model evaluation, limitations, and trade-offs
Ability to explain models and results in simple, intuitive terms
Experience with Explainable AI (XAI) concepts and techniques
Strong judgment on when Generative AI adds value-and when it does not
Understanding of agentic AI concepts (design-level grasp is sufficient)
Data & Use Cases
Time series and temporal data analysis
Numerical and high-dimensional tabular datasets
Anomaly detection, trend modeling, and outlier identification
Applying ML outputs directly to business decision-making
Programming & Tools
Python (practical proficiency; perfection not required)
PyTorch (preferred over TensorFlow)
scikit-learn, XGBoost, pandas
Experience with Python ML libraries in time series and tabular contexts
Deployment experience on Azure or a comparable cloud platform
To avoid: PySpark, Databricks
Apply Now
Apply Now
We are seeking a business-oriented Machine Learning Engineer with strong fundamentals in classical ML and modern AI, including Generative AI. This role focuses on applying AI to real-world business problems, particularly in time series, tabular, and document-based data.
The ideal candidate is comfortable working autonomously, collaborating across international teams, and translating complex analytical findings into clear, actionable insights for stakeholders.
Key Responsibilities
Analyze complex datasets (time series, tabular, numerical, and temporal data) to identify patterns, trends, anomalies, and outliers
Design, build, evaluate, and explain ML models aligned to real business use cases
Break down ambiguous or complex business problems into solvable ML tasks
Interpret model results, analyze errors, and connect outcomes back to business impact
Communicate findings clearly to non-technical stakeholders and ask the right follow-up questions
Propose and iterate on AI-driven solutions based on data insights
Collaborate closely with cross-functional and international teams in English
We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to [email protected] learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.
Skills and Requirements
Strong fundamentals in machine learning concepts
Hands-on experience with deep learning / neural networks and modern AI approaches
Solid understanding of model evaluation, limitations, and trade-offs
Ability to explain models and results in simple, intuitive terms
Experience with Explainable AI (XAI) concepts and techniques
Strong judgment on when Generative AI adds value-and when it does not
Understanding of agentic AI concepts (design-level grasp is sufficient)
Data & Use Cases
Time series and temporal data analysis
Numerical and high-dimensional tabular datasets
Anomaly detection, trend modeling, and outlier identification
Applying ML outputs directly to business decision-making
Programming & Tools
Python (practical proficiency; perfection not required)
PyTorch (preferred over TensorFlow)
scikit-learn, XGBoost, pandas
Experience with Python ML libraries in time series and tabular contexts
Deployment experience on Azure or a comparable cloud platform
To avoid: PySpark, Databricks
Apply Now
Apply Now