Role Overview:

We are looking for a highly analytical and innovative Data Scientist to join our growing data team. In this role, you will leverage statistical analysis, machine learning, predictive modeling, and advanced analytics to solve complex business problems and generate actionable insights. You will collaborate with cross-functional teams to design data-driven solutions that improve decision-making, optimize business processes, and drive strategic initiatives. The ideal candidate should have strong programming skills, hands-on experience with machine learning algorithms, data preprocessing, feature engineering, and model deployment, along with the ability to communicate technical findings to both technical and non-technical stakeholders.

Key Responsibilities:

- Analyze structured and unstructured datasets to identify trends, patterns, and business opportunities.

- Design, develop, and deploy machine learning models for classification, regression, clustering, forecasting, recommendation systems, and anomaly detection.

- Perform data cleaning, preprocessing, transformation, and feature engineering to improve model performance.

- Build predictive and statistical models to solve complex business challenges across multiple domains.

- Develop scalable data pipelines for model training, validation, and deployment.

- Conduct exploratory data analysis (EDA) to derive meaningful insights from large datasets.

- Evaluate model performance using appropriate statistical metrics and continuously optimize model accuracy.

- Collaborate with Data Engineers to build robust data pipelines and ensure high-quality data availability.

- Work closely with business stakeholders to understand requirements and translate them into analytical solutions.

- Design and conduct A/B testing and hypothesis testing to evaluate business initiatives.

- Build dashboards and visualizations to present analytical findings and model outcomes.

- Automate recurring analytical processes and reporting workflows.

- Research and implement new statistical techniques, algorithms, and analytical methodologies.

- Monitor deployed models for performance degradation and retrain models when necessary.

- Document methodologies, model assumptions, technical approaches, and business outcomes.

- Ensure data governance, privacy, and compliance with organizational standards.

- Participate in code reviews and contribute to analytical best practices.

Required Technical Skills:

- Strong proficiency in Python for data analysis and machine learning.

- Hands-on experience with SQL for querying and manipulating large datasets.

- Experience with Pandas, NumPy, SciPy, Scikit-learn, Statsmodels, and related Python libraries.

- Knowledge of supervised and unsupervised machine learning techniques.

- Strong understanding of statistical modeling and probability.

- Experience with feature engineering, model selection, hyperparameter tuning, and cross-validation.

- Familiarity with time-series forecasting techniques.

- Experience with clustering, dimensionality reduction, and recommendation systems.

- Knowledge of model evaluation metrics and statistical testing.

- Experience using Jupyter Notebook or similar development environments.

- Familiarity with Git for version control.

- Understanding of REST APIs for model integration is an advantage.

- Exposure to Docker and cloud platforms is desirable.

Preferred Skills:

- Strong analytical and critical thinking abilities.

- Excellent problem-solving skills with attention to detail.

- Ability to simplify complex analytical concepts for business stakeholders.

- Strong communication and presentation skills.

- Experience working in Agile environments.

- Ability to manage multiple analytical projects simultaneously.

- Self-motivated with a passion for data-driven decision-making.

Qualifications:

- Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, Information Technology, Engineering, or a related quantitative field.

- Certifications in Data Science, Machine Learning, or Advanced Analytics are an added advantage.

Desired Candidate Profile:

- 36 years of experience in Data Science, Predictive Analytics, or Statistical Modeling.

- Proven experience building and deploying machine learning models in production environments.

- Strong expertise in data manipulation, exploratory analysis, and predictive modeling.

- Experience working with large-scale datasets and solving complex business problems.

- Ability to collaborate effectively with engineering, product, and business teams.

- Passion for continuous learning and staying updated with advancements in data science and analytics.

- Strong ownership mindset with the ability to deliver high-quality analytical solutions in a fast-paced environment.