Data Scientist

Weekday 1; Bengaluru; On-site; INR 6,000,000 - 10,000,000; Full time

Job summary

Weekday 1 is seeking an experienced Data Scientist in Bengaluru to drive end-to-end data science initiatives. The role demands translating complex business problems into scalable data-driven solutions, mentoring juniors, and delivering impactful analytics across product, engineering, and leadership teams.

The candidate should have 10–14 years of professional experience with strong Python, ML, and MLOps expertise, plus excellent communication to explain insights to non-technical stakeholders.

Qualifications

• 10–14 years of experience in Data Science, Machine Learning, Advanced Analytics, or a closely related field.
• Strong expertise in Python and data science libraries such as Pandas, NumPy, Scikit-learn.
• Hands‑on experience developing and deploying machine learning models in production environments.
• Strong SQL skills and experience working with large‑scale datasets and databases.

Responsibilities

• Lead the end-to-end development of data science solutions, from data exploration and feature engineering to model development, validation, deployment, and monitoring.
• Design and implement statistical models, machine learning algorithms, predictive models, and optimization techniques for complex business problems.
• Analyze large and diverse datasets to identify patterns, trends, opportunities, and actionable insights.
• Collaborate with product, engineering, business, and leadership stakeholders to understand requirements and translate them into analytical solutions.
• Develop robust data pipelines and workflows required for data preparation, experimentation, and model development.
• Evaluate model performance using appropriate statistical and business metrics and continuously improve model accuracy and scalability.
• Present complex analytical findings and recommendations clearly to both technical and non-technical stakeholders.
• Mentor junior and mid-level data scientists and contribute to technical best practices across the team.
• Stay updated with emerging developments in machine learning, analytics, and AI, and identify opportunities to incorporate relevant technologies into existing solutions.

Skills

Data Science

Machine Learning

Advanced Analytics

Python

Pandas

NumPy

Scikit-learn

SQL

Data Pipelines

MLOps

Tools

TensorFlow

PyTorch

Spark

AWS

Job description

This role is for one of Weekday’s clients
Salary range: Rs 6000000 - Rs 10000000 (ie INR 60 - 100 LPA)

Min Experience: 10+ years
Location: Bengaluru, Karnataka, India
JobType: full-time

We are looking for an experienced and highly skilled Data Scientist with 10–14 years of professional experience to join our team. The ideal candidate will have a strong background in data science, statistical modelling, machine learning, and advanced analytics, with the ability to translate complex business problems into scalable, data-driven solutions.

You will work closely with engineering, product, business, and analytics teams to identify opportunities where data can create measurable impact. The role requires strong technical depth, business understanding, and the ability to independently drive data science initiatives from problem definition through deployment and monitoring.

Requirements

Key Responsibilities

• Lead the end-to-end development of data science solutions, from data exploration and feature engineering to model development, validation, deployment, and monitoring.
• Design and implement statistical models, machine learning algorithms, predictive models, and optimization techniques for complex business problems.
• Analyze large and diverse datasets to identify patterns, trends, opportunities, and actionable insights.
• Collaborate with product, engineering, business, and leadership stakeholders to understand requirements and translate them into analytical solutions.
• Develop robust data pipelines and workflows required for data preparation, experimentation, and model development.
• Evaluate model performance using appropriate statistical and business metrics and continuously improve model accuracy and scalability.
• Present complex analytical findings and recommendations clearly to both technical and non-technical stakeholders.
• Mentor junior and mid-level data scientists and contribute to technical best practices across the team.
• Stay updated with emerging developments in machine learning, analytics, and AI, and identify opportunities to incorporate relevant technologies into existing solutions.

Must-Have Skills

• 10–14 years of experience in Data Science, Machine Learning, Advanced Analytics, or a closely related field.
• Strong expertise in Python and data science libraries such as Pandas, NumPy, Scikit-learn, and similar frameworks.
• Strong understanding of statistics, probability, hypothesis testing, regression, classification, clustering, and predictive modelling.
• Hands‑on experience developing and deploying machine learning models in production environments.
• Strong SQL skills and experience working with large‑scale datasets and databases.
• Experience with data preprocessing, feature engineering, model validation, experimentation, and performance optimization.
• Strong understanding of machine learning lifecycle and MLOps practices.
• Ability to independently own complex data science projects and work effectively with cross‑functional teams.
• Excellent analytical, problem‑solving, communication, and stakeholder management skills.

Good‑to‑Have Skills

• AI and practical exposure to AI‑driven applications.
• Experience with Generative AI, Large Language Models (LLMs), NLP, or AI‑powered products.
• Familiarity with deep learning frameworks such as TensorFlow or PyTorch.
• Exposure to cloud platforms such as AWS, Azure, or Google Cloud.
• Experience with distributed data processing technologies such as Spark.
• Knowledge of MLOps tools and practices, including model deployment, monitoring, versioning, and CI/CD.
• Experience working with recommendation systems, forecasting, optimization, or real‑time analytics.
• Exposure to responsible AI, model explainability, and AI governance.