Senior Data Scientist

We are looking for a highly skilled Senior Data Scientist to join our team and help us build scalable machine learning models and data-driven solutions. In this role, you will be responsible for leading complex analytical projects from conception to deployment. You will collaborate closely with product, engineering, and business stakeholders to uncover insights, develop predictive models, and drive strategic decision-making. As a senior member of the team, you will also play a key role in mentoring junior data scientists and shaping our data architecture.

What you'll do:

• Model Development: Design, build, and deploy advanced machine learning models (predictive, prescriptive, NLP, or computer vision depending on business needs) to solve complex business problems.
• Data Analysis & Exploration: Mine and analyze large, complex datasets to discover trends, patterns, and actionable business insights.
• End-to-End Execution: Own the full data science lifecycle, from data collection and cleaning to feature engineering, model training, validation, deployment, and monitoring.
• Cross-Functional Collaboration: Partner with product managers, software engineers, and business leaders to translate business requirements into technical data solutions.
• Mentorship & Leadership: Guide and mentor junior data scientists and analysts, fostering a culture of continuous learning and technical excellence.
• A/B Testing & Experimentation: Design and analyze rigorous experiments to measure the impact of new product features and algorithms.
• Data Strategy: Contribute to the design and improvement of data pipelines, data architecture, and MLOps practices.

Your Expertise:

• 5+ years of professional experience as a Data Scientist or Machine Learning Engineer, with a proven track record of deploying models into production.
• Programming: Advanced proficiency in Python (or R) and standard data science libraries (e.g., Pandas, NumPy, Scikit-Learn).
• Database querying: Expert-level knowledge of SQL and experience working with relational and non-relational databases.
• Machine Learning: Deep understanding of ML algorithms (regression, classification, clustering, ensemble methods, deep learning) and frameworks (e.g., TensorFlow, PyTorch, XGBoost).
• Cloud & Big Data: Experience with cloud computing platforms (AWS, Google Cloud, or Azure) and big data technologies (Spark, Hadoop, Snowflake).
• Communication: Strong ability to communicate complex technical concepts to non-technical stakeholders and translate business problems into data science frameworks.
• Experience with MLOps tools and model deployment frameworks (e.g., MLflow, Kubeflow, Docker, Kubernetes).
• Proficiency with data visualization tools (e.g., Tableau, Power BI, Looker).
• Familiarity with version control (Git) and CI/CD pipelines.

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