Senior Data Science Lead - R01571251

Brillio; Bengaluru; On-site; INR 3,000,000 - 5,200,000; Full time

Job summary

Brillio in Bengaluru seeks a Senior Data Science Lead to guide complex analytics initiatives. You will spearhead model development, validation, and deployment, driving business impact with advanced statistics and ML. The role requires 8+ years in data science and demonstrated leadership across projects.

You will mentor juniors, collaborate with cross-functional teams, and translate business needs into scalable data strategies using Python, Spark, and modern ML tools.

Qualifications

• 8+ years in data science with leadership experience.
• Strong Python and PySpark expertise for data processing.
• Proficient in statistical analysis and hypothesis testing.
• Experience with ML model development and deployment.

Responsibilities

• Lead design and deployment of advanced ML models with measurable business impact.
• Oversee end-to-end data science lifecycles from exploration to validation.
• Apply regression, forecasting, and classification techniques to business problems.
• Collaborate with teams to align data strategies with goals.
• Mentor junior data scientists and conduct code reviews.
• Present findings with clear visuals to stakeholders.

Skills

Python

PySpark

Statistical analysis

Regression techniques

Classification

ML frameworks

Forecasting

Data quality monitoring

R / R Studio

SAS / SPSS

Evidently AI / Great Expectations

Model deployment

Education

Bachelor’s degree in CS / Stats / Math / DS

Microsoft Certified: Azure Data Scientist Associate

TensorFlow Developer Certificate

Tools

TensorFlow

PyTorch

Sci-Kit Learn

CNTK

Keras

MXNet

R Studio

SAS

SPSS

Job description

Senior Data Science Lead

Job requirements

Experience Range

With at least 8 years of experience in data science and advanced analytics, including recent leadership roles spanning up to 12 years

Key Responsibilities

• Lead the design, development, and implementation of advanced statistical models and machine learning solutions to address complex business challenges and deliver measurable impact
• Drive end-to-end data science project lifecycles, overseeing data exploration, hypothesis testing, feature engineering, model selection, and validation
• Apply regression, classification, and forecasting techniques such as ARIMA, ARIMAX, exponential smoothing, and decision trees to generate predictive analytics and actionable insights
• Collaborate with cross-functional teams to translate business objectives into actionable data science strategies and ensure alignment with organizational goals
• Build, evaluate, and deploy scalable machine learning models using Python, PySpark, R, TensorFlow, PyTorch, and Sci-Kit Learn
• Monitor data quality, bias detection, and model performance using Great Expectations and Evidently AI, ensuring robust analytics outcomes
• Mentor and guide junior data scientists, providing technical leadership, conducting code reviews, and promoting best practices in statistical analysis and machine learning
• Present findings and insights to stakeholders through clear visualizations and presentations, facilitating data-driven decision making

Required Skills

• Advanced proficiency in Python and PySpark for data processing and modeling
• Expertise in statistical analysis, including hypothesis testing, t-tests, and z-tests
• Strong knowledge of regression techniques (linear, logistic) and classification algorithms (decision trees, SVM)
• Hands-on experience with probabilistic graphical models for complex data relationships
• Proficiency with machine learning frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, and MXNet
• Experience with forecasting methods including exponential smoothing, ARIMA, and ARIMAX
• Competence in data quality and monitoring tools such as Great Expectations and Evidently AI
• Working knowledge of SAS or SPSS for statistical analysis and computing
• Familiarity with R and R Studio for advanced analytics
• Understanding of distance metrics such as Hamming, Euclidean, and Manhattan

Preferred Skills

• Experience deploying machine learning models in production environments using KubeFlow or BentoML
• Expertise in developing scalable data pipelines for machine learning workflows
• Knowledge of advanced feature engineering and dimensionality reduction techniques such as PCA and t-SNE
• Background in model interpretability and explainable AI methodologies (e.g., SHAP, LIME)
• Exposure to real-time analytics and streaming data platforms such as Apache Kafka or Spark Streaming

Desired Qualifications

• Bachelor’s degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related discipline
• Microsoft Certified: Azure Data Scientist Associate or TensorFlow Developer Certificate (preferred)
• Formal training or certification in advanced statistical analysis or machine learning frameworks