Senior Data Scientist
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Role Overview
We are seeking an experienced and highly motivated Senior Data Scientist to design, develop, and scale advanced machine learning solutions that deliver measurable business outcomes. The ideal candidate will possess deep expertise across classical machine learning techniques, architect end-to-end ML pipelines, and build production-grade AI/ML solutions at scale.
This role requires close collaboration with business stakeholders, analytics teams, data engineers, and technology leaders to solve complex business problems across domains such as Finance and Supply Chain.
Key Responsibilities
Work closely with business stakeholders to identify opportunities and translate business challenges into AI/ML solutions.
Design, develop, and deploy machine learning models using structured and unstructured data.
Apply advanced statistical and machine learning techniques including regression, classification, clustering, recommendation systems, and time-series forecasting.
Architect and implement end-to-end ML pipelines covering data ingestion, feature engineering, model training, validation, deployment, monitoring, and retraining.
Build scalable and production-ready ML systems using cloud-native technologies and MLOps best practices.
Drive model deployment through Docker, CI/CD pipelines, cloud-based serving infrastructure, and automated monitoring frameworks.
Establish robust monitoring mechanisms for model performance, data quality, drift detection, and retraining strategies.
Evaluate trade-offs between model accuracy, latency, scalability, and operational costs to deliver optimal business solutions.
Partner with data engineering teams to develop scalable data pipelines and feature stores.
Mentor junior data scientists and provide technical leadership on AI/ML initiatives.
Present findings, recommendations, and technical solutions to both business and executive stakeholders.
Qualifications
Education
Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
Experience
8+ years of experience in Data Science, Machine Learning, or Artificial Intelligence.
Proven experience delivering AI/ML solutions in enterprise environments.
Demonstrated success in developing and deploying production-grade machine learning systems.
Technical Skills
Machine Learning & Statistics
Deep expertise in classical machine learning techniques including:
Regression
Classification
Clustering
Time-Series Forecasting
Ensemble Methods
Feature Engineering
Model Optimization
Strong understanding of statistics, probability, experimentation, and predictive modeling.
Programming & Frameworks
Proficiency in Python and SQL.
Experience with machine learning frameworks such as:
Scikit-learn
XGBoost
TensorFlow
PyTorch
MLOps & Deployment
Experience designing and implementing end-to-end ML pipelines.
Strong hands-on experience with:
Docker
CI/CD pipelines
Model serving and deployment frameworks
Drift monitoring and model lifecycle management
Experience with cloud platforms such as AWS, Azure, or GCP.
Data Engineering
Strong understanding of data architecture, ETL processes, APIs, and large-scale data processing.
Experience working with structured, semi-structured, and unstructured datasets.
Preferred Qualifications
Functional knowledge in at least one business domain:
Finance
Supply Chain
Experience with Generative AI and Large Language Models (LLMs) is a plus.
Experience working in consulting or customer-facing environments.
Key Competencies
Strong solution architecture and system design capabilities.
Ability to architect AI/ML solutions at scale.
Expertise in balancing accuracy, latency, scalability, and cost considerations.
Excellent communication and stakeholder management skills.
Strong analytical thinking and problem-solving abilities.
Ability to lead cross-functional teams and mentor junior team members.