Job Title: Senior Data Scientist

About the Role

We are seeking a highly skilled and experienced Senior Data Scientist to lead high-impact analytics initiatives and shape the future of data-driven decision-making within our organization. In this role, you will design and deploy scalable machine learning solutions, conduct advanced experimentation, and guide the team on best practices in data science.

As a senior team member, you will work closely with business leaders, data engineers, and junior scientists to solve complex problems, uncover insights, and create models that drive measurable business outcomes.

Key Responsibilities

• Lead end-to-end development of machine learning models—from problem definition to production deployment
• Architect scalable and maintainable ML pipelines and systems in collaboration with data engineering and DevOps teams
• Conduct advanced causal inference, multivariate testing, and uplift modeling to support product and growth experiments
• Apply advanced statistical and machine learning techniques to solve complex business challenges
• Collaborate with business stakeholders to translate strategic questions into analytical frameworks
• Mentor and coach junior data scientists, review code, and enforce best practices for modeling and reproducibility
• Drive adoption of MLOps practices, including model versioning, monitoring, and CI/CD pipelines
• Contribute to the development of the organization’s data science strategy and technical roadmap
• Present findings to senior leadership through compelling storytelling and data visualization

Required Qualifications

Education:

• Bachelor’s degree in Data Science, Computer Science, Mathematics, Statistics, Engineering, or a related quantitative field
• Master’s or PhD degree preferred and considered a strong advantage

Experience:

• 4–7 years of hands-on experience in data science, with a track record of deploying machine learning solutions in production
• Demonstrated experience leading analytics projects and collaborating cross-functionally

Technical Skills & Tools

• Programming: Advanced proficiency in Python or R (including libraries such as scikit-learn, XGBoost, statsmodels, TensorFlow, or PyTorch)
• SQL & Data Access: Strong SQL skills and experience working with structured and semi-structured data
• Machine Learning & Statistics: Deep understanding of supervised/unsupervised learning, time series forecasting, NLP, clustering, and statistical inference
• MLOps & Productionization: Experience with model deployment, monitoring, CI/CD workflows, and tools like MLflow, Kubeflow, or SageMaker
• Big Data & Distributed Computing: Familiarity with Spark, Databricks, or Dask for large-scale data processing
• Cloud Platforms: Practical experience with AWS, GCP, or Azure cloud services for data science workflows
• Visualization & Storytelling: Proficiency with Tableau, Power BI, or Python libraries such as Plotly, Seaborn, and Matplotlib
• Version Control & Workflow: Git, Jupyter, notebooks, and collaborative data science platforms

Soft Skills & Competencies

• Strong leadership and mentorship abilities; able to guide teams and influence stakeholders
• Excellent communication skills with the ability to simplify complex concepts for business audiences
• Business acumen to align data science work with strategic priorities
• Proactive mindset with the ability to drive initiatives independently and deliver under tight timelines
• Comfortable working in fast-paced, collaborative environments

Preferred Qualifications (Nice to Have):

• Experience working with real-time data pipelines, streaming analytics, or online learning models
• Exposure to data privacy, ethical AI, or governance practices
• Contributions to open-source projects, technical blogs, or research papers in data science or ML
• Familiarity with deep learning, computer vision, or recommendation systems in production settings