Data Scientist (W2)

We are looking for an experienced Data Scientist with 4+ years of experience in data analysis, statistical modeling, machine learning, and predictive analytics. The ideal candidate will have strong programming skills in Python, experience working with large datasets, and the ability to transform complex data into actionable business insights.
The candidate will collaborate with data engineers, business stakeholders, and cross-functional teams to develop, deploy, and maintain data-driven solutions that support business objectives.

Key Responsibilities
Collect, clean, process, and analyze large structured and unstructured datasets.
Develop and implement machine learning models for classification, regression, clustering, forecasting, and predictive analytics.
Perform exploratory data analysis (EDA) to identify patterns, trends, and business opportunities.
Apply statistical techniques and mathematical modeling to solve complex business problems.
Build, validate, evaluate, and optimize machine learning models.
Develop data pipelines and automate data preparation and model evaluation processes.
Use data visualization tools to communicate findings and recommendations to stakeholders.
Collaborate with data engineers and software developers to integrate machine learning models into production systems.
Monitor model performance, identify data drift, and improve model accuracy over time.
Document methodologies, experiments, model performance, and technical findings.
Follow best practices for data quality, model validation, reproducibility, and responsible AI.
Required Technical Skills
Experience: 4+ years of hands-on experience in Data Science, Machine Learning, or Advanced Analytics.
Programming: Strong proficiency in Python, including Pandas, NumPy, SciPy, and Scikit-learn.
Machine Learning: Experience with supervised and unsupervised learning algorithms.
Statistics: Strong understanding of probability, statistical analysis, hypothesis testing, and regression.
Data Analysis: Experience with exploratory data analysis, feature engineering, data preprocessing, and data quality validation.
SQL: Proficiency in writing complex SQL queries, joins, aggregations, and data transformations.
Data Visualization: Experience with Power BI, Tableau, Matplotlib, Seaborn, or Plotly.
Model Evaluation: Knowledge of cross-validation, hyperparameter tuning, precision, recall, F1-score, ROC-AUC, and other evaluation metrics.
Machine Learning Frameworks: Experience with Scikit-learn and familiarity with TensorFlow or PyTorch.
Big Data: Exposure to Spark, PySpark, Databricks, or similar platforms is preferred.
Cloud Platforms: Familiarity with AWS, Azure, or Google Cloud for data processing and machine learning workloads.
Version Control: Experience with Git and collaborative development practices.