Job Requirements 7-10 years, 3pm to 12 midnight

Location: Chennai, Hyderabad

Experience: 7-10 years, 3 pm to 12 midnight

Position demands development data-driven insights and innovations within the Sidekick product, focusing on analytics, dashboarding, and Generative AI research and development.

Key Responsibilities

· Develop and maintain data analytics and dashboards for the Sidekick product, providing key insights to stakeholders.

· Conduct research and development in Generative AI to improve Sidekick features and functionality.

· Analyze large datasets to identify trends, patterns, and opportunities for improvement.

· Collaborate with product and engineering teams to translate insights into actionable strategies.

· Develop and implement machine learning models to support Sidekick's capabilities.

Requirements

· R Shiny, R programming, Dashboarding, AI Development

· 7-10 years experience in data science, with a focus on analytics and model development.

· Experience in programming skills such as Python, including experience with relevant libraries (e.g., pandas, scikit-learn, TensorFlow/PyTorch).

· Experience with data visualization tools

· Experience with machine learning and Generative AI techniques.





Evaluation Topics and Weight (Questions and evaluation are based on below)

Topics for Evaluation Percentage Mandatory/Non Mandatory

Data science and data visualization tool,

Analytics Modeling 25 Mandatory

Expertise in Python and libraries such as pandas, NumPy, and dplyr.

25 Mandatory

Gen AI, Machine learning: Knowledge of algorithms, frameworks (e.g., Scikit-learn, TensorFlow), and model deployment 25 Mandatory

Communication skills, translate business problems into data-driven solutions 25 Mandatory

Sample Questions for Training Model

- Name the algorithm commonly used for classification tasks in machine learning?
- You are tasked with predicting customer churn for a subscription service. Which machine learning algorithm would be most suitable for this classification problem?
- You are analyzing a dataset with highly imbalanced classes. What technique can help improve the performance of your machine learning model?
- You are given a dataset with 100 features, but only a few are relevant for prediction. What technique can you use to reduce the number of features?
- You are working on a time series forecasting problem. Which model is most used for such tasks?

*By default, two coding questions will be asked. Howev

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