Lead Data Scientist – Machine Learning & Analytics

This role is for one of our clientsIndustry: Technology, Information and MediaSeniority level: Mid-Senior levelMin Experience: 4 yearsLocation: BengaluruJobType: full-timeWe’re looking for an experienced

Lead Data Scientist

to join our dynamic and fast-growing data team. In this role, you'll lead impactful initiatives across the full machine learning lifecycle—solving real-world problems using data at scale. From ideation to deployment, you’ll bring clarity to business decisions through cutting-edge modeling and analytics.If you're passionate about applying machine learning to business challenges, mentoring others, and driving outcomes with data, we want you on our team.What You’ll Do

Lead end-to-end data science projects

: Define problems, explore datasets, build predictive models, and deploy them into production systems.

Develop robust ML models

using Python and its rich ecosystem (e.g., NumPy, Scikit-learn, Pandas, TensorFlow, PyTorch) tailored to real business use cases.

Collaborate cross-functionally

with engineering, product, and business teams to align models with strategic goals and ensure seamless integration.

Conduct deep data exploration (EDA)

to uncover trends, surface opportunities, and shape data-driven strategies.

Drive model performance monitoring

, retraining pipelines, and A/B testing to ensure continuous improvement and stability post-deployment.

Mentor junior data scientists

by reviewing code, guiding methodology, and promoting best practices in project execution.

Translate insights into action

by presenting clear, concise results and recommendations to stakeholders and leadership.

Stay ahead of the curve

by researching emerging tools, frameworks, and ML techniques to elevate team capabilities.What You Bring

4–9 years of experience

in data science, applied machine learning, and statistical modeling in a business or tech environment.Strong command of

Python and its ML ecosystem

(Pandas, Scikit-learn, NumPy, Matplotlib, Seaborn).Solid grasp of

ML techniques

— supervised/unsupervised learning, regression/classification, ensemble methods, model validation, feature engineering.Hands-on experience deploying models in production systems.Proficient in working with

large datasets

, performing data cleaning, transformation, and ensuring data quality throughout the pipeline.Excellent communication and stakeholder engagement skills — you can clearly explain complex technical concepts to non-technical audiences.Experience with

version control systems

(e.g., Git) and familiarity with

cloud platforms

(AWS, GCP, or Azure).Strong analytical thinking and structured problem-solving ability.Bonus SkillsExposure to

deep learning frameworks

like TensorFlow or PyTorch.Experience with

distributed computing tools

(e.g., Apache Spark, Dask).Familiarity with

ML Ops

principles and production model monitoring.Knowledge of

data visualization and BI tools

like Tableau, Power BI, or Looker.