Data Scientist

Role Overview: Welcome to TP, a global hub of innovation and empowerment, where you can maximize your impact. With a remarkable 10 billion annual revenue and a global team of 500,000 employees serving 170 countries in over 300 languages, TP leads in intelligent, digital-first solutions. As a globally certified Great Place to Work in 72 countries, the culture thrives on diversity, equity, and inclusion. Your unique perspective and talent are valued to complete the vision for a brighter, digitally driven tomorrow. Key Responsibilities: - Design data preprocessing pipelines for multi-modal datasets including text, audio, video, and images. - Engineer features and transformations for model training, fine-tuning, and evaluation. - Apply data augmentation, synthetic data generation, and bias mitigation techniques. - Develop and evaluate ML models for classification, ranking, clustering, and anomaly detection tailored to operational use cases. - Expertise in power analysis, significance testing, and advanced methods such as bootstrapping and Bayesian inference. - Create offline/online evaluation metrics (accuracy, F1, precision/recall, latency, fairness metrics) to track model performance. - Support RLHF pipelines by integrating human feedback into model optimization loops. - Collaborate with engineering teams to integrate models into production pipelines. - Design automated monitoring, retraining triggers, and continuous improvement loops to maintain model quality in dynamic environments. - Build dashboards and reporting frameworks for real-time monitoring of model and data health. - Define labeling strategies, taxonomy structures, and annotation guidelines for supervised learning. - Partner with annotation platforms to ensure accuracy, throughput, and SLA compliance. - Perform statistical quality checks (inter-annotator agreement, sampling-based audits). - Partner with clients, product teams, and operations leads to translate business problems into data/ML solutions. - Work with Legal/Privacy teams to ensure datasets meet regulatory and ethical AI standards. - Contribute to internal best practices, reusable frameworks, and knowledge bases for AI/ML Operations. Qualifications Required: - Masters or Ph.D. in Computer Science, Data Science, Applied Mathematics, or related fields. - 5+ years of experience in ML/AI data science with a focus on operational deployment. - Programming: Python (NumPy, Pandas, Scikit-learn, PyTorch/TensorFlow), SQL. - ML Techniques: Supervised/unsupervised learning, NLP, CV, RLHF. - MLOps Tools: MLflow, Kubeflow, SageMaker, Vertex AI, or similar. - Data Handling: Spark, Dataflow, or equivalent big data frameworks. - Visualization & Reporting: Tableau, Looker, or similar BI tools. - Experience with Generative AI model evaluation (LLMs, diffusion models). - Knowledge of Responsible AI frameworks (bias/fairness metrics, explainability). - Exposure to Trust & Safety operations or high-volume data environments.,