Junior Data Scientist

• Model Development & Implementation: Design, develop, train, validate, and deploy advanced Machine Learning models (e.g., classification, regression, clustering, deep learning) to solve complex business problems.
• Data Wrangling & Analysis: Perform comprehensive Exploratory Data Analysis (EDA), data cleaning, feature engineering, and transformation on large, complex, and sometimes unstructured datasets.
• Coding & Automation: Write production-quality, highly efficient, and scalable code primarily in Python for data processing, analysis, and model creation.
• Experimentation & Optimization: Conduct A/B testing, hypothesis testing, and rigorous model validation, continually iterating and tuning algorithms to maximize performance, accuracy, and efficiency.
• Collaboration & Communication: Work closely with cross-functional teams (e.g., product managers, engineers, business stakeholders) to define project scope, interpret model results, and clearly present data-driven recommendations to both technical and non-technical audiences.
• Deployment & MLOps: Collaborate with ML/Data Engineers to deploy, monitor, and maintain ML models in a production environment, ensuring stability and performance over time.

We are seeking an innovative and results-driven Data Scientist with a strong focus on Machine Learning and deep proficiency in Python. You will be instrumental in transforming complex data into actionable insights, building predictive models, and driving business strategy using cutting-edge analytical techniques. This role is for a hands-on individual who is excited to move models from the lab into production., * Programming: Expert proficiency in Python and its core data science libraries: Pandas, NumPy, and SciPy.

• Machine Learning: Deep, practical experience with popular ML frameworks and libraries: scikit-learn, TensorFlow, or PyTorch.
• Statistics & Math: Strong foundation in statistical modeling, probability, hypothesis testing, regression analysis, and multivariate calculus/linear algebra for understanding model mechanics.
• Databases & Querying: Proficiency in SQL for extracting, manipulating, and preparing data from relational databases. Experience with NoSQL databases is a plus.
• Big Data/Cloud: Experience with big data tools (Spark, Hadoop) and cloud computing platforms (AWS, Azure, or GCP) for scalable ML workflows.
• Visualization: Ability to create clear, compelling data visualizations using tools like Matplotlib, Seaborn, Tableau, or Power BI to communicate insights.

Education & Experience

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field.
• [Number] years of professional experience as a Data Scientist or in a highly quantitative role.

Soft Skills (Your X-Factor)

• Curiosity: A strong passion for data and an inherent curiosity to explore, question, and challenge assumptions.
• Problem-Solving: Excellent analytical and structured thinking skills to tackle complex, ambiguous business problems.
• Storytelling: The ability to translate complex statistical and ML outputs into simple, business-relevant narratives and recommendations.