Sr Data Scientist

Equinix; Bengaluru; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Equinix in Bengaluru is seeking a Senior Data Scientist to design and deploy statistical, forecasting, and machine-learning solutions at scale. You will build production-ready models and generate actionable insights to support planning and decision-making across teams.

The role requires strong expertise in statistics, time-series analysis, probabilistic forecasting, ML, and Google Cloud Platform (GCP). You will own the model lifecycle and communicate results to both technical and non-technical

Qualifications

• 4+ years of experience in data science, statistics, or ML.
• Strong foundation in statistics with production experience.
• Experience with time series and probabilistic forecasting; ML methods.

Responsibilities

• Build and deploy statistical and probabilistic forecasting models for demand and capacity.
• Apply time series methods including ARIMA/SARIMA and state-space models.
• Define modeling assumptions, forecast uncertainty, and evaluation methods.
• Develop ML models for forecasting and prediction (regression, trees, neural networks).
• Own model lifecycle: development, backtesting, deployment, monitoring, and retraining.
• Create visualizations to communicate forecasts, trends, and model outputs.
• Collaborate with product managers, engineers, and business teams on analytics needs.
• Translate model results into actionable business insights.

Skills

Time series analysis

Probabilistic forecasting

Machine learning

Python

SQL

Statistics

MLOps

GCP

Education

Bachelor’s or Master’s in CS or Stats

Tools

SQL

Python

GCP

Job description

The Senior Data Scientist designs and deploys statistical, forecasting, and machine learning solutions to solve complex business problems. This role works with cross-functional teams to build production-ready models, generate actionable insights, and improve planning and decision-making at scale.

The role requires strong expertise in statistics, time series analysis, probabilistic forecasting, machine learning, and Google Cloud Platform (GCP).

Responsibilities

Statistical Modeling & Forecasting

• Build and deploy statistical and probabilistic forecasting models for demand, capacity, and trend analysis
• Apply time series methods including regression-based models, ARIMA/SARIMA, and state-space models
• Define modeling assumptions, forecast uncertainty, and evaluation methods
• Build and deploy machine learning models for forecasting and prediction, including regression, tree-based models, gradient boosting and neural networks
• Select statistical or ML approaches based on accuracy, interpretability, robustness, and operational needs
• Develop features and run experiments to improve model performance

MLOps & Production Deployment

• Own the model lifecycle, including development, backtesting, deployment, monitoring, and retraining
• Implement model monitoring, performance tracking, and data drift detection
• Ensure models are versioned, reproducible, and production-ready
• Perform exploratory data analysis, feature engineering, and hypothesis testing on large, complex datasets
• Identify data requirements for forecasting and predictive modeling, including key inputs, data gaps, and quality needs
• Work with big data technologies and distributed data processing to support scalable modeling
• Partner with data engineering teams to ensure data quality, availability, and efficient data pipelines
• Create visualizations to communicate forecasts, trends, and model outputs
• Translate model results into actionable insights for business and operational stakeholders

Collaboration & Stakeholder Engagement

• Work closely with product managers, engineers, and business teams to define forecasting and analytics requirements
• Communicate modeling approaches, assumptions, and results clearly to technical and non-technical stakeholders

Qualifications

• 4+ years of experience in data science, applied statistics, or machine learning
• Strong foundation in statistics and experience applying statistical methods in production
• Proven experience with time series analysis and probabilistic forecasting
• Hands-on experience with machine learning models such as regression, boosting, and neural networks
• Experience owning production data science models, including deployment and monitoring
• Experience working with large datasets and using SQL and Python for analytical and modeling workflows
• Proficiency in Python and common DS/ML libraries (e.g., pandas, NumPy, scikit-learn, statsmodels, PyTorch/TensorFlow)
• Experience working on Google Cloud Platform (GCP)
• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field