Data Scientist

What can you expect in a Data Scientist role with TaskUs:

Think of yourself as someone who will design the end-user access layer of the Business Intelligence projects and perform the detailed design of requested reports, so not just anyone is qualified for this role! We make sure we get the best of the best, after all, we are a ridiculously good company so we make sure our employees are top notch. So come on, now we need your full concentration because it’s time to imagine what it’s like being a Data Scientist.

Imagine yourself going to work with one thing on your mind: you want to collect and analyze data that can help understand the heart of the business. As you tackle your new tasks for the day, you know that it will lead to one thing: that you will work with data architecture to design aggregation strategy that supports business definitions, scalability and usability of the front end solution.

Key Responsibilities:

• AI/ML Development & Research: Design, develop, and deploy advanced machine learning and deep learning models for complex business problems. Implement and optimize Large Language Models (LLMs) and Generative AI solutions. Build agentic AI systems with autonomous decision-making capabilities. Conduct research on emerging AI technologies and their practical applications. Perform model evaluation, validation, and continuous improvement
• Cloud Infrastructure & Full-Stack Development: Architect and implement scalable cloud-native ML/AI solutions on AWS, Azure, or GCP. Develop full-stack applications integrating AI models with modern web technologies. Build and maintain ML pipelines using cloud services (SageMaker, ML Engine, etc.). Implement CI/CD pipelines for ML model deployment and monitoring. Design and optimize cloud infrastructure for high-performance computing workloads.
• Data Engineering & Database Management: Design and implement data pipelines for large-scale data processing. Work with both SQL and NoSQL databases (PostgreSQL, MongoDB, Cassandra, etc.). Optimize database performance for ML workloads and real-time applications. Implement data governance and quality assurance frameworks. Handle streaming data processing and real-time analytics.
• Leadership & Collaboration: Mentor junior data scientists and guide technical decision-making. Collaborate with cross-functional teams including product, engineering, and business stakeholders. Present findings and recommendations to technical and non-technical audiences. Lead proof-of-concept projects and innovation initiatives.

Required Qualifications:

• 5+ years of hands-on experience in data science and machine learning - 5 years in data science
• 3+ years of experience with deep learning frameworks and neural networks
• 2+ years of experience with cloud platforms and full-stack development (BPO preferred), healthcare domain, BFSI.
• Machine Learning: Scikit-learn, XGBoost, LightGBM, advanced ML algorithms
• Deep Learning: TensorFlow, PyTorch, Keras, CNN, RNN, LSTM, Transformers
• Large Language Models: GPT, BERT, T5, fine-tuning, prompt engineering
• Generative AI: Stable Diffusion, DALL-E, text-to-image, text generation
• Programming: Python (expert), R, Java/Scala, JavaScript/TypeScript
• Cloud Platforms: AWS (SageMaker, EC2, S3, Lambda), Azure ML, or Google Cloud AI
• Databases: SQL (PostgreSQL, MySQL), NoSQL (MongoDB, Cassandra, DynamoDB)
• MLOps: MLflow, Kubeflow, Model versioning, A/B testing frameworks
• Experience with vector databases and embeddings (Pinecone, Weaviate, Chroma)
• Knowledge of LangChain, LlamaIndex, or similar LLM frameworks
• Experience with model compression and edge deployment
• Familiarity with distributed computing and parallel processing
• Experience with computer vision and NLP applications
• Knowledge of federated learning and privacy-preserving ML
• Experience with quantum machine learning
• Problem-Solving & Innovation
• Communication & Leadership
• Technical Excellence
• Expertise in MLOps and production ML system design is a plus!
• Big Data: Spark, Hadoop, Kafka, streaming data processing is a plus!
• Agentic AI: Multi-agent systems, reinforcement learning, autonomous agents is a plus!

Education / Certifications:

• Bachelor's Degree in Computer Science, Data Science, Statistics, Mathematics, or related field. Master's or PhD is a plus!

Work Location / Work Schedule / Travel:

• Mid shift
• WFH but must be amenable to report onsite when needed