Lead Data Scientist

Weekday AI (YC W21); Hyderabad; On-site; INR 2,500,000 - 3,000,000; Full time

Job summary

Weekday AI (YC W21) in Hyderabad seeks an experienced Lead Data Scientist with 8-13 years to lead the development and implementation of advanced data science and ML solutions. The ideal candidate will translate complex business problems into scalable, data-driven strategies.

You will collaborate with engineering, product, analytics and business teams to identify opportunities where data and AI can create measurable impact.

Qualifications

• 8-13 years of professional experience in Data Science or closely related field.
• Strong hands-on expertise in Data Science with Python.
• Extensive experience building and deploying ML models in production.
• Strong understanding of deep learning techniques and neural networks.
• Excellent knowledge of statistical modeling, probability, experimentation, feature engineering, and model evaluation.
• Strong problem-solving and analytical skills for ambiguous business problems.
• Experience leading cross-functional teams and providing technical leadership.
• Understanding of the ML lifecycle from data prep to deployment and monitoring.

Responsibilities

• Lead design, development, testing, and deployment of advanced DS/ML models to solve complex business problems.
• Develop robust DS solutions using Python and libraries like Pandas, NumPy, Scikit-learn.
• Build and optimize ML models for classification, regression, clustering, recommendation, forecasting, anomaly detection, etc.
• Design and implement Deep Learning architectures using TensorFlow, PyTorch, or equivalents.
• Perform EDA, feature engineering, statistical analysis, model selection, and hyperparameter tuning.
• Collaborate with data engineering to ensure data pipelines and data quality for modeling.
• Partner with product and business stakeholders to translate requirements into DS solutions.
• Lead technical discussions on model architecture, experimentation, deployment, and monitoring.
• Mentor junior data scientists and promote best practices across the team.
• Stay updated with ML innovations including generative AI and NLP.

Skills

Data science expertise

ML in production

Deep learning

Model evaluation

Cross-functional leadership

Mentoring

Tools

Python

Pandas

NumPy

Scikit-learn

TensorFlow

PyTorch

Job description

Job Description:

This role is for one of Weekday's clients
Salary range: Rs 2500000 - Rs 3000000 (ie INR 25 - 30 LPA)

Min Experience: 8+ years
Location: Hyderabad
JobType: full-time

We are looking for an experienced and highly skilled Lead Data Scientist with 8-13 years of experience to lead the development and implementation of advanced data science and machine learning solutions. The ideal candidate will have strong expertise in Data Science using Python, Machine Learning, and Deep Learning, along with the ability to translate complex business problems into scalable, data-driven solutions.

In this role, you will work closely with engineering, product, analytics, and business teams to identify opportunities where data and AI can create measurable impact. You will also provide technical leadership, mentor data scientists, and contribute to the design and evolution of machine learning systems.

Key Responsibilities

• Lead the design, development, testing, and deployment of advanced data science and machine learning models to solve complex business problems.
• Develop robust data science solutions using Python, leveraging libraries such as Pandas, NumPy, Scikit-learn, and other relevant frameworks.
• Build and optimize Machine Learning models for classification, regression, clustering, recommendation, forecasting, anomaly detection, and other use cases.
• Design and implement Deep Learning architectures using frameworks such as TensorFlow, PyTorch, or equivalent technologies.
• Perform exploratory data analysis, feature engineering, statistical analysis, model selection, and hyperparameter optimization.
• Evaluate model performance using appropriate statistical and business metrics and continuously improve model accuracy, scalability, and reliability.
• Work with large and complex datasets to identify meaningful patterns, trends, and insights.
• Collaborate with data engineering teams to establish effective data pipelines and ensure high-quality data availability for modeling.
• Partner with product and business stakeholders to understand requirements and convert them into practical data science solutions.
• Lead technical discussions around model architecture, experimentation strategies, deployment approaches, and model monitoring.
• Mentor junior and mid-level data scientists, conduct technical reviews, and promote best practices across the data science team.
• Stay updated with advancements in machine learning, deep learning, generative AI, and emerging data science methodologies.

Must-Have Skills

• 8-13 years of professional experience in Data Science, Machine Learning, or a closely related field.
• Strong hands-on expertise in Data Science with Python.
• Extensive experience building and deploying Machine Learning models in production environments.
• Strong understanding and practical experience with Deep Learning techniques and neural network architectures.
• Excellent knowledge of statistical modeling, probability, experimentation, feature engineering, and model evaluation.
• Strong problem-solving and analytical skills with the ability to work on ambiguous and complex business problems.
• Experience working with cross-functional teams and providing technical leadership.
• Strong understanding of the complete machine learning lifecycle, from data preparation and experimentation to deployment and monitoring.

Good-to-Have Skills

• Experience with Natural Language Processing (NLP), including text classification, sentiment analysis, embeddings, information extraction, or language models.
• Exposure to Generative AI, Large Language Models, or modern NLP architectures.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Familiarity with MLOps, model deployment, monitoring, and scalable machine learning infrastructure.