Data Scientist - Tech Lead
- Compensation
- Not disclosed
- Location
- Córdoba Province, AR
- Work setup
- Hybrid
- Seniority
- Lead
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Data science
- Posted
Skills
Must have
- 6+ years of experience as a Data Scientist, including at least 2 years leading, mentoring, or setting technical direction for a team.
- Proven track record of taking models to production and owning them post-deployment, not only in research or notebook environments.
- Strong development skills in Python (pandas, scikit-learn, PyTorch or TensorFlow) and SQL over large datasets.
- Solid statistical foundation: experimentation design, A/B testing, hypothesis testing, and causal inference.
- Experience with the full ML lifecycle: feature engineering, validation, monitoring, drift detection, and retraining.
- Hands-on experience applying LLMs or GenAI to real production use cases, with clear criteria for when they are and aren't the right tool.
- Experience working with distributed processing and large volumes of data (Spark / Databricks or equivalent).
- Experience in cloud environments (AWS, GCP, or Azure).
- Ability to communicate methodology, results, and uncertainty to non-technical stakeholders.
- Experience defining technical standards and reviewing the work of other data scientists.
- Advanced English Level.
Full description
Data Scientist - Tech Lead
About Promtior
Promtior is at the forefront of the Agentic AI revolution, delivering cutting-edge solutions that transform businesses across industries — bridging technology and people to turn AI into a real competitive advantage. Since 2023, we've helped organizations boost operational efficiency through tailored Agentic AI solutions, from discovery and development to full implementation, across three core lines: AI Product Delivery (predictive analytics, intelligent automation, AI-driven chatbots, image processing), AI Department as a Service (LATAM-based teams that integrate directly with our clients' teams), and AI Adoption Consulting (helping companies find and act on AI opportunities across their business).
We're a team held together by genuine relationships, constant exchange, and shared learning. We believe innovation happens when people feel included and collaborate with purpose, and we aim for professional growth to go hand in hand with personal well-being.
About the Role
We're looking for a Data Engineer to join our Engineering team.
Key Responsibilities
You will lead a team of data scientists, defining technical direction, modeling approaches, and quality standards across the team's work. You will own the full lifecycle of data science initiatives, from framing an ambiguous business problem into a measurable one, through model design and validation, to deployment and monitoring in production. You will design and oversee experimentation frameworks, including A/B testing and causal inference, to make sure decisions are backed by sound methodology rather than intuition. You will define standards for reproducibility, code quality, model documentation, and performance monitoring, including drift detection and retraining strategies. You will decide when a problem calls for classical machine learning, statistical modeling, or LLM-based approaches, and justify those trade-offs to both technical and business audiences. You will partner closely with data engineering, product, and business stakeholders to secure data availability and translate results into decisions, and you will mentor the team through code reviews, technical guidance, and career development.
Required Experience
- 6+ years of experience as a Data Scientist, including at least 2 years leading, mentoring, or setting technical direction for a team.
- Proven track record of taking models to production and owning them post-deployment, not only in research or notebook environments.
- Strong development skills in Python (pandas, scikit-learn, PyTorch or TensorFlow) and SQL over large datasets.
- Solid statistical foundation: experimentation design, A/B testing, hypothesis testing, and causal inference.
- Experience with the full ML lifecycle: feature engineering, validation, monitoring, drift detection, and retraining.