Job description

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.

Requirements