Senior Data Scientist - Remote Trading Analytics & ML Engineering

Are you passionate about applying machine learning to financial markets? Join our innovative team as a Data Scientist to transform trading analytics through advanced predictive modeling. You'll build sophisticated ML solutions that drive strategic decision-making and optimize trading performance while working remotely with cutting-edge technologies. Key Responsibilities - Collaborate with business departments to translate requirements into effective machine learning solutions for our trading platform. - Enhance existing models to improve accuracy, efficiency, and business value metrics across our trading ecosystem. - Develop predictive algorithms that forecast user behavior patterns including churn probability, conversion rates, and lifetime value calculations. - Transform prototype models into production-ready systems with robust data pipelines from preprocessing to prediction delivery. - Conduct comprehensive exploratory data analysis on client behavior using Python 3.10+, SQL, and PySpark 3.4+. - Design and implement automated tools to streamline the entire ML model lifecycle from development to deployment. - Apply MLOps practices to ensure consistent quality, monitoring, and reliable deployment of machine learning models. - Translate complex analytical findings into clear, actionable insights for non-technical stakeholders and business teams. Required Skills - 1-3 years of professional experience building machine learning solutions with measurable business impact. - Strong proficiency in Python and its data science ecosystem (NumPy, Pandas, Scikit-learn, XGBoost). - Advanced SQL skills for querying, manipulating, and analyzing large financial datasets. - Experience with Git for version control, collaborative development, and code review processes. - Upper-intermediate or higher English proficiency in both written and verbal communication. - Ability to work independently while maintaining clear communication in a remote environment. - Solid understanding of statistical methods and their practical applications in predictive modeling. - Bachelor's degree or higher in Computer Science, Statistics, Mathematics, or related quantitative field. Nice to Have - Experience with deep learning frameworks such as PyTorch 2.0+ or TensorFlow 2.x for advanced model development. - Knowledge of PySpark for distributed processing of large-scale financial datasets. - Docker containerization skills for creating reproducible and isolated development environments. - AWS cloud computing experience, particularly with SageMaker, Lambda, or other ML-focused services. - Background in financial client scoring models, risk assessment, and credit evaluation systems. - Successful participation in Kaggle competitions demonstrating practical ML problem-solving abilities. - Experience analyzing trading metrics, financial market data, and time-series financial information. - Proficiency with time-series analysis and forecasting techniques using libraries like Prophet or statsmodels. - Understanding of A/B testing methodologies and causal inference in user behavior analysis. - Familiarity with feature stores, ML model serving technologies, and real-time prediction systems. Why Join Us Working with our team means tackling meaningful trading challenges with direct market impact. You'll apply advanced machine learning techniques in a flexible, remote environment that values innovation and continuous improvement. We offer competitive compensation, professional development opportunities, and the chance to shape next-generation trading technology alongside talented professionals from diverse backgrounds.