Data Scientist

We are seeking a motivated and experienced Data Scientist to join our dynamic Data Science team. This role is crucial for transforming large, complex datasets into actionable strategies that drive business value for our customers through our innovative brand intelligence platform. The ideal candidate has 3-5 years of practical experience building, validating, and deploying machine learning models in a production environment, and possesses the communication skills necessary to translate complex technical findings into clear business recommendations.

🔑 Key Responsibilities

• Data Preparation and Exploration: Responsible for the end-to-end data lifecycle, including gathering, cleaning, and preprocessing large, structured, and unstructured datasets from diverse sources. This involves conducting robust Exploratory Data Analysis (EDA) to identify trends, correlations, outliers, and data quality issues that impact modeling accuracy.
• Model Development and Deployment: Design, build, and rigorously validate various statistical and machine learning models (e.g., classification, regression, time series forecasting) using best-in-class techniques. Collaborate closely with Data Engineering and Software Engineering teams to integrate, test and deploy models into production systems, ensuring reliability and scalability.
• Business Insights and Communication: Act as a critical link between technical data and business objectives. Proactively analyze datasets to uncover opportunities for growth and efficiency, then clearly communicate complex results and strategic recommendations to both technical and non-technical stakeholders, primarily through compelling data storytelling and visualization.
• Experimentation and Monitoring: Develop and execute A/B tests or other statistical experiments to measure the impact of product changes or business interventions. Additionally, build and maintain monitoring systems to track the performance and stability of deployed models, implementing tuning or retraining as necessary.

🏅 Required Qualifications

• Proven Experience: 3-5 years of hands-on professional experience in a dedicated Data Scientist, Applied Scientist, or highly quantitative role. Experience should demonstrate an ability to take models from conception through to production deployment.
• Educational Foundation: Bachelor's or Master's degree in a quantitative field such as Computer Science, Statistics, Applied Mathematics, Economics, or a related Engineering discipline. A strong theoretical background in statistical modeling, probability theory, and experimental design is essential.
• Problem-Solving and Ownership: Demonstrated ability to independently define, scope, and execute complex data science projects that directly address business challenges. Must be comfortable managing multiple competing priorities in a fast-paced, research-oriented environment.

🛠️ Technical Skills

• Programming & Tools: Proficiency in Python (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch). MS Office (Excel, PowerPoint, etc.)
• Data Management: Experience with and knowledge of SQL and Database/ Data Storage and Retrieval best practices (including complex joins, window functions, and query optimization) for extracting and manipulating data from relational and NoSQL databases. Familiarity with Big Data platforms (e.g., Spark/PySpark) is highly desirable.
• Machine Learning Expertise: Extensive, hands-on experience applying a variety of machine learning techniques (e.g., bayesian models, logistic regression, structural equation modeling) and a deep understanding of model evaluation metrics (e.g., precision, recall, RMSE) and validation methods.
• Data Visualization: Experience creating clear and impactful dashboards and reports using BI tools or visualization libraries in Python (Matplotlib, Seaborn).