Talent Worx is a growing services & recruitment consulting firm, we are hiring for our client which is a globally leading provider of financial intelligence, data analytics, and AIdriven solutions, empowering businesses worldwide with insights for confident decision making. Join to work on cutting edge technologies, drive digital transformation, and shape the future of global markets.

Requirements

Job SummaryAs a leader in the EDO, Collection Platforms & AI Cognitive Engineering team, you will own the vision and delivery of enterprise-scale Data Science and GenAI solutions. You will define the data science strategy , champion best practices , mentor a world-class team of data scientists and ML engineers , and drive production-ready AI/ML products from ideation through deployment. This role involves working in a global team that values thoughtful risk-taking and self-initiative.Key Responsibilities

Shape and execute the Data Science roadmap for ML, GenAI, and ASR, including tooling, methodologies, and best practices.
Architect, develop, and deploy large-scale ML and GenAI pipelines, including Voice Activity Detection (VAD), Speaker Diarization, and ASR models.
Own the full data science lifecycle, from opportunity identification and requirement gathering to modeling, deployment, monitoring, and optimization.
Drive MLOps practices, including CI/CD for models, feature stores, monitoring, and automated rollback strategies.
Mentor and develop junior and mid-level data scientists, fostering a culture of continuous learning and collaboration.
Partner with cross-functional stakeholders to align on project goals, timelines, and SLAs.

Core Requirements

8+ years of hands-on experience in Data Science/AI, with at least 3 years in a senior or leadership role.
Proven expertise in developing and deploying ASR systems, including training models (e.g., Whisper), designing VAD pipelines, and implementing Speaker Diarization (e.g., Pyannote).
Deep knowledge of large language models (e.g., OpenAI, Anthropic, Llama), prompt engineering, fine-tuning, and embedding-based retrieval.
Expert proficiency in Python and its core libraries, including NumPy, Pandas, SpaCy, scikit-learn, PyTorch/TF 2, and Hugging Face Transformers.
Strong understanding of ML & deep learning architectures like NLP transformers and GNNs.
Experience with orchestration and deployment tools such as Docker, Kubernetes, Airflow, and cloud services (AWS/GCP/Azure).

Good to Have

A Master's or Ph.D. in a related technical field (Computer Science, Statistics, etc.).
Prior experience in the Economics/Financial industry.
Public contributions on platforms like GitHub, Kaggle, or technical blog

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