Lead Data Scientist - Machine Learning

Our client, a cutting-edge financial technology firm, is seeking an exceptional Lead Data Scientist with a strong background in Machine Learning to join their highly collaborative team in **Chicago, Illinois**. This role will involve leading complex data science projects from inception to deployment, driving innovation in algorithmic trading, risk management, and customer analytics. You will be instrumental in shaping the company's data-driven strategy and mentoring a talented team of data scientists.

Responsibilities:

Lead the design, development, and implementation of advanced machine learning models and algorithms to solve challenging business problems in finance. Conduct exploratory data analysis, feature engineering, and model selection for predictive and prescriptive analytics. Oversee the end-to-end data science lifecycle, including data acquisition, cleaning, feature engineering, model training, validation, and deployment. Mentor and guide junior data scientists, providing technical expertise and fostering a culture of learning and innovation. Collaborate closely with product managers, engineers, and business stakeholders to define project requirements and deliver impactful solutions. Stay abreast of the latest research and advancements in machine learning, artificial intelligence, and data science. Develop and maintain robust, scalable, and production-ready ML pipelines. Present findings and recommendations to senior management and diverse audiences. Ensure the ethical and responsible use of data and AI technologies. Qualifications:

Master's or Ph.D. in Computer Science, Statistics, Mathematics, Physics, or a related quantitative field. 7+ years of hands-on experience in data science and machine learning, with a proven track record of delivering successful projects. Expert proficiency in Python or R, and relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch, Keras, Pandas, NumPy). Deep understanding of various ML algorithms (e.g., regression, classification, clustering, deep learning, reinforcement learning) and their underlying principles. Experience with big data technologies (e.g., Spark, Hadoop) and SQL. Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps practices. Excellent analytical, problem-solving, and critical thinking skills. Strong leadership, communication, and interpersonal skills, with the ability to effectively manage and mentor a team. Experience in the financial services or fintech industry is a strong plus. This is a challenging and rewarding opportunity to make a significant impact on the future of financial technology. Be part of a forward-thinking organization that values innovation and professional growth.