Lead Data scientist at Weekday AI

Mumbai, maharashtra, India -

Full Time

Start Date

Immediate

Expiry Date

16 Jul, 26

Salary

0.0

Posted On

17 Apr, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, Machine Learning, Python, R, Propensity Modelling, Predictive Analytics, Statistical Modeling, Supervised Learning, Unsupervised Learning, Ensemble Methods, Feature Engineering, Model Governance, Data Visualization, Strategic Thinking, Mentorship, Analytical Solutions

Industry

technology;Information and Internet

Description

This role is for one of the Weekday's clients Min Experience: 8 years Location: Mumbai JobType: full-time We are seeking a highly skilled and experienced Lead Data Scientist to drive advanced analytics initiatives and lead the end-to-end lifecycle of data science projects. This role is ideal for someone who combines deep technical expertise with strategic thinking, and has a proven track record in model building, model development, and propensity modelling across complex business environments. As a Lead Data Scientist, you will be responsible for designing, developing, and deploying scalable machine learning models that generate actionable insights and directly impact business outcomes. You will work closely with cross-functional teams including product, engineering, marketing, and business stakeholders to identify opportunities, define problem statements, and translate them into robust analytical solutions. A key focus of this role is model building and development. You will lead the creation of predictive and prescriptive models using structured and unstructured data, ensuring high performance, accuracy, and scalability. This includes selecting appropriate algorithms, performing feature engineering, optimizing model performance, and validating results through rigorous testing frameworks. You should be comfortable working with a variety of machine learning techniques, including supervised and unsupervised learning, ensemble methods, and advanced statistical modeling. Another critical aspect of this role is expertise in propensity modelling. You will design and implement models that predict customer behavior, such as likelihood to purchase, churn, conversion, or engagement. These models will be central to driving personalized marketing strategies, customer segmentation, and revenue optimization. Experience in building and deploying propensity models in real-world business scenarios is essential. In addition to hands-on technical work, you will mentor and guide a team of data scientists and analysts, fostering a culture of innovation, collaboration, and continuous learning. You will also play a key role in setting best practices for model governance, documentation, and lifecycle management. Key Responsibilities: Lead end-to-end data science projects from problem definition to deployment Build and optimize machine learning models with a strong focus on performance and scalability Develop and implement propensity models to drive customer-centric decision-making Collaborate with stakeholders to translate business problems into analytical solutions Mentor junior team members and provide technical leadership Ensure best practices in model validation, monitoring, and maintenance Required Skills & Qualifications: 8–15 years of experience in data science, machine learning, or related fields Strong expertise in model building and model development using Python/R Proven experience in propensity modelling and predictive analytics Deep understanding of statistical methods, machine learning algorithms, and data structures Experience with data visualization tools and communicating insights to non-technical stakeholders Strong problem-solving skills and ability to work in a fast-paced environment

Responsibilities

Lead end-to-end data science projects from problem definition to deployment while building and optimizing scalable machine learning models. Collaborate with cross-functional stakeholders to implement propensity models that drive customer-centric decision-making and business outcomes.