Senior Data Scientist

Job Description

Key Responsibilities: Leverage traditional machine learning techniques (decision trees, random forests, SVM, logistic regression) to build predictive models. Analyze complex datasets to identify key trends and provide data-driven insights for business decisions. Partner with cross-functional teams to understand business needs and translate them into effective ML solutions. Rigorously evaluate and optimize models for accuracy and real-world performance. Apply statistical and mathematical principles to enhance analytical outcomes. Communicate findings and model insights to both technical and non-technical stakeholders. Desired Skills and Qualifications: Deep understanding of traditional machine learning algorithms (decision trees, clustering, Naive Bayes, SVM, etc.). Strong proficiency in mathematics and statistics (linear algebra, probability, statistical inference). Proven ability to translate business problems into analytical frameworks and actionable insights. Expertise in data processing, cleaning, and feature engineering using Python (pandas, NumPy). Hands-on experience building and deploying ML models using Python and libraries like scikit-learn. Excellent analytical, problem-solving, and critical thinking skills. Strong communication and collaboration skills to effectively convey technical results. Familiarity with ML tools and frameworks (Scikit-learn, XGBoost, LightGBM). Experience with model evaluation, validation, and optimization techniques. Business acumen and the ability to apply ML to solve real-world business challenges. Preferred Qualifications: Bachelors or Masters degree in Data Science, Statistics, Mathematics, Computer Science, or a related quantitative field. 3+ years of experience as a Data Scientist with a focus on machine learning. Demonstrated experience in driving business decisions through data analysis and modeling. Familiarity with cloud platforms (AWS, Azure, GCP) for data science workflows.