We are seeking a highly experienced and results-driven Senior Data Scientist with a strong background in machine learning, advanced analytics, and data-driven product

development. The ideal candidate will have 7+ years of experience in solving real-world problems using data science and ML techniques across various domains such as finance,

healthcare, retail, or tech.

You will work closely with cross-functional teams including engineering, product management, and business stakeholders to design, develop, and deploy machine learning solutions that

directly impact the company’s goals.

Key Responsibilities

Lead end-to-end development of machine learning models, from data preprocessing to deployment and monitoring.
Apply statistical and ML techniques to solve business problems such as classification, regression, clustering, recommendation, NLP, forecasting, etc.
Design and execute experiments to evaluate model performance, interpret results, and iterate as needed.
Collaborate with data engineers to build robust, scalable data pipelines and model serving architecture.
Work with product managers and business leads to translate high-level requirements into actionable data science projects.
Mentor junior data scientists and promote best practices in model development, reproducibility, and documentation.
Communicate findings and technical solutions effectively to non-technical stakeholders.

Required Qualifications

Master's or PhD in Computer Science, Statistics, Applied Mathematics, or related field.
7+ years of hands-on experience in data science and machine learning in a production environment.
Proficient in Python and major data science libraries (Pandas, NumPy, Scikit-learn, XGBoost, TensorFlow/PyTorch).
Strong understanding of ML algorithms, model evaluation metrics, overfitting/underfitting, bias-variance tradeoff, etc.
Experience with SQL and working with large datasets using Spark or similar frameworks.
Proven experience deploying models via REST APIs or cloud platforms (AWS/GCP/Azure).
Solid foundation in statistics, data visualization, and hypothesis testing.

Preferred Qualifications

Experience with ML Ops tools (MLflow, Kubeflow, SageMaker, Vertex AI, etc.).
Familiarity with version control (Git), containerization (Docker), and CI/CD pipelines.
Background in time-series forecasting, deep learning, or NLP.
Experience with A/B testing, experimentation platforms, and causal inference techniques.