Data Science Manager

Role requirement :

• The role requires a Data Science and Machine Learning professional with 7+ years of experience in Python, ML/AI, statistics, and advanced analytics.
• The candidate should have expertise in developing commonly used ML models, analyzing large datasets.
• Key skills include Pricing Analytics, Forecasting, Segmentation, A/B Testing, and Test & Learn frameworks, along with hands-on experience in ML techniques such as Linear Regression, Decision Trees, Random Forest, Gradient Boosting, and K-Means Clustering.
• Strong proficiency in Python, SQL, Excel, and visualization tools is essential, with experience in libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Seaborn, and Plotly. Exposure to Tableau, Power BI, Generative AI, and Prompt Engineering would be an added advantage.
• Beyond technical expertise, the role requires strong communication, stakeholder management, problem-solving abilities, and a proactive mindset.

The ideal candidate should be able to translate analytics into business recommendations, engage in strategic discussions and solutioning, and effectively balance technical delivery with business impact.

Other considerations

• Strong hands-on Data Science / ML / Python skills are non-negotiable.
• Strong methodological thinking and communication skills are critical. The work is complex from a methodology perspective, so the individual needs to be able to understand complex problems and communicate the approach and outcomes in a simple, effective way to stakeholders.
• A strong research mindset is required. While there are established testing methodologies we use today, the expectation is to continuously research, experiment, and identify new and better approaches.
• Strong problem-solving and stakeholder management skills are essential. The person should be able to quickly break down ambiguous problems, identify solutions, and effectively manage stakeholder expectations and communication.