Senior Data Scientist

About the Role

We are seeking an experienced and highly analytical Senior Data Scientist to research, design, and deploy machine learning models and AI-powered solutions that drive critical business decisions. In this role, you will own the end-to-end lifecycle of data science initiatives, from data exploration and model development to deployment and monitoring in production environments.

You will work on complex challenges involving predictive analytics, forecasting, risk assessment, optimization, and intelligent automation. The ideal candidate combines strong quantitative expertise with practical business acumen and enjoys transforming complex data into actionable insights for stakeholders across multiple departments.

This position is ideal for someone who thrives in a fast-paced environment, enjoys solving real-world problems with data, and is passionate about building scalable AI solutions that create measurable business impact.

Responsibilities

Predictive Modeling & Analytics

Design, develop, and deploy machine learning models for forecasting, risk analysis, classification, optimization, and decision support.

Build predictive analytics solutions that improve business performance and operational efficiency.

Analyze large and complex datasets to identify trends, patterns, and growth opportunities.

AI & Intelligent Automation

Develop AI-driven workflows and intelligent agents to automate business processes.

Design systems that incorporate human oversight, governance, and reliability controls.

Evaluate and implement emerging AI technologies to enhance organizational capabilities.

Data Engineering & Orchestration

Create scalable data pipelines and workflows for data processing and model deployment.

Build and manage multi-step AI workflows using modern machine learning and large language model frameworks.

Collaborate with engineering teams to integrate AI solutions into production systems.

Data Visualization & Business Insights

Transform analytical findings into clear dashboards, reports, and visualizations.

Present recommendations and insights to technical and non-technical stakeholders.

Support strategic decision-making through data-driven storytelling.

MLOps & Model Management

Manage the full machine learning lifecycle, including experimentation, deployment, monitoring, and performance optimization.

Implement model versioning, tracking, and governance processes.

Monitor production models for performance degradation, drift, and reliability.

Production Deployment

Develop APIs and services for model serving and AI applications.

Deploy and maintain scalable cloud-based machine learning solutions.

Ensure production systems meet performance, security, and reliability requirements.

Qualifications

Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, Finance, or a related quantitative field.

6+ years of experience in Data Science, Machine Learning, Artificial Intelligence, or Quantitative Analytics.

Proven experience deploying machine learning models and AI solutions into production environments.

Strong analytical and problem-solving abilities.

Ability to work independently in a remote environment while collaborating effectively with distributed teams.

Required Skills

Machine Learning & Modeling

Experience with predictive modeling, time-series forecasting, regression, classification, clustering, and optimization techniques.

Strong knowledge of machine learning algorithms, feature engineering, and model evaluation.

Experience with model explainability and interpretability techniques.

Programming & Tools

Advanced Python programming skills.

Strong experience with Pandas, NumPy, Scikit-learn, and related data science libraries.

Familiarity with machine learning lifecycle tools and experiment tracking platforms.

Exposure to deep learning frameworks such as PyTorch or TensorFlow.

Databases & Data Management

Strong SQL skills.

Experience working with relational databases such as PostgreSQL and MySQL.

Ability to manage and analyze large-scale structured and unstructured datasets.

Data Visualization

Experience with dashboarding and reporting tools such as Power BI, Tableau, Streamlit, or similar platforms.

Ability to communicate complex analytical findings through clear visualizations.

Software Engineering

Strong coding standards and software development best practices.

Experience building APIs and integrating machine learning services into applications.

Ability to troubleshoot complex issues across data pipelines and production systems.

Communication

Excellent written and verbal communication skills.

Ability to explain technical concepts to business stakeholders.

Experience presenting findings and recommendations to leadership teams.

Preferred Skills

Master's degree or Ph.D. in a quantitative discipline.

Experience in FinTech, financial services, risk analytics, lending, or investment management.

Knowledge of cloud platforms such as AWS, Azure, or Google Cloud.

Experience with Docker, CI/CD pipelines, and infrastructure automation.

Familiarity with monitoring and observability tools.

Experience working with NoSQL databases, graph databases, or vector databases.

Knowledge of REST APIs, ETL pipeline design, and modern data architecture.

Experience with large language models (LLMs), AI agents, and generative AI applications.

Benefits

Fully remote work environment.

Competitive compensation package.

Flexible working hours.

Opportunity to work with cutting-edge AI and machine learning technologies.

Professional development and learning opportunities.

Collaborative and innovative team culture.

Exposure to high-impact projects that influence strategic business decisions.