Senior Data Science Analyst

About the Role We are seeking an experienced Senior Data Science Analyst to lead and execute advanced analytics initiatives that drive strategic business decisions. This role demands a blend of technical expertise, leadership, and business acumen to deliver impactful data-driven solutions. Key Responsibilities: • Project Leadership: Lead end-to-end data science projects, from problem definition and data exploration to model development and deployment, ensuring alignment with business objectives. • Predictive Modeling & Machine Learning: Design and implement advanced models for classification, regression, and clustering to address complex business challenges. • Big Data Ecosystem Expertise: Utilize Hadoop, HDFS, Hive, Sqoop, Spark (PySpark, SparkR, SparkSQL), and notebooks (Jupyter, Zeppelin) to process and analyze large-scale datasets. • Feature Engineering & Data Integration: Perform detailed analysis and feature engineering on structured and unstructured data, integrating diverse data sources to build robust analytical solutions. • Collaboration & Communication: Work closely with business partners to understand requirements, translate them into analytical solutions, and communicate findings through presentations and reports. • Mentorship: Guide and mentor junior data scientists, fostering a culture of continuous learning and innovation within the team. Required Qualifications: • Experience: Minimum of 5 years in data science roles, with hands-on experience in R/Python on Hadoop platforms. • Technical Skills: Proficiency in statistical programming languages (R, Python), machine learning frameworks (e.g., scikit-learn, TensorFlow), and big data tools (Hadoop, Spark). • Machine Learning Expertise: Extensive experience in developing and deploying predictive models, including classification, regression, and clustering techniques. • Data Engineering Knowledge: Understanding of data pipelines, ETL processes, and data integration techniques. • Cloud Technologies: Familiarity with cloud platforms such as AWS, Azure, or GCP is advantageous. • Education: Bachelor's degree or higher in Computer Science, Information Systems, Mathematics, or a related field. Preferred Qualifications: • Advanced Degree: Master's or Ph.D. in a quantitative field. • Cloud Platforms: Experience with cloud technologies like AWS, Azure, or GCP. • Data Engineering: Knowledge of data engineering principles and tools. • ML Ops: Experience with ML Ops practices and tools like MLFlow or Kubeflow. Soft Skills: • Communication: Strong verbal and written communication skills to effectively convey complex technical concepts to non-technical stakeholders. • Collaboration: Ability to work effectively in multidisciplinary teams, demonstrating leadership and teamwork. • Problem-Solving: Exceptional analytical and problem-solving abilities, with a data-driven mindset. • Adaptability: Ability to thrive in a fast-paced, dynamic environment, managing multiple projects simultaneously.