Lead Data Scientist

Job Description

Job Overview We are looking for a dynamic and innovative Full Stack Data Scientist with 45 years of experience who excels in end-to-end data science solutions. The ideal candidate is a tech-savvy professional passionate about leveraging data to solve complex problems, develop predictive models, and drive business impact in the MarTech domain. Key Responsibilities 1. Data Engineering & Preprocessing Collect, clean, and preprocess structured and unstructured data from various sources. Perform advanced feature engineering, outlier detection, and data transformation. Collaborate with data engineers to ensure seamless data pipeline development. 2. Machine Learning Model Development Design, train, and validate machine learning models (supervised, unsupervised, deep learning). Optimize models for business KPIs such as accuracy, recall, and precision. Innovate with advanced algorithms tailored to marketing technologies. 3. Full Stack Development Build production-grade APIs for model deployment using frameworks like Flask, FastAPI, or Django. Develop scalable and modular code for data processing and ML integration. 4. Deployment & Operationalization Deploy models on cloud platforms (AWS, Azure, or GCP) using tools like Docker and Kubernetes. Implement continuous monitoring, logging, and retraining strategies for deployed models. 5. Insight Visualization & Communication Create visually compelling dashboards and reports using Tableau, Power BI, or similar tools. Present insights and actionable recommendations to stakeholders effectively. 6. Collaboration & Teamwork Work closely with marketing analysts, product managers, and engineering teams to solve business challenges. Foster a collaborative environment that encourages innovation and shared learning. 7. Continuous Learning & Innovation Stay updated on the latest trends in AI/ML, especially in marketing automation and analytics. Identify new opportunities for leveraging data science in MarTech solutions. Qualifications Educational Background Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field. Technical Skills Programming Languages: Python (must-have), R, or Julia; familiarity with Java or C++ is a plus. ML Frameworks: TensorFlow, PyTorch, Scikit-learn, or XGBoost. Big Data Tools: Spark, Hadoop, or Kafka. Cloud Platforms: AWS, Azure, or GCP for model deployment and data pipelines. Databases: Expertise in SQL and NoSQL (e.g., MongoDB, Cassandra). Visualization: Mastery of Tableau, Power BI, Plotly, or D3.js. Version Control: Proficiency with Git for collaborative coding. Experience 4–5 years of hands-on experience in data science, machine learning, and software engineering. Proven expertise in deploying machine learning models in production environments. Experience in handling large datasets and implementing big data technologies. Soft Skills Strong problem-solving and analytical thinking. Excellent communication and storytelling skills for technical and non-technical audiences. Ability to work collaboratively in diverse and cross-functional teams. Preferred Qualifications Experience with Natural Language Processing (NLP) and Computer Vision (CV). Familiarity with CI/CD pipelines and DevOps for ML workflows. Exposure to Agile project management methodologies. Role & responsibilities Preferred candidate profile