Lead Data Scientist
Lead Data Scientist
teksands.ai7-9 Years
- Posted 8 hours ago
- Be among the first 10 applicants
Job Description
- Bachelor's or higher degree in Computer Science, Electrical Engineering or a quantitative field, with a strong research orientation and at least 7 years of relevant experience in data science
- Deep expertise in modern machine learning techniques, mathematical foundations, and hands-on experience with leading ML frameworks (such as scikit-learn, Keras, TensorFlow, PySpark, MLlib, etc.)
- Extensive experience with distributed computing platforms and scalable data processing (e.g. Spark)
- Advanced programming skills, preferably in Python, Scala, or similar languages, with a track record of delivering robust, production-grade solutions
- Proven ability to lead and deliver large-scale machine learning projects end-to-end—from problem definition, data exploration, and modeling, to deployment and production monitoring
- Demonstrated experience in mentoring junior data scientists, including setting technical direction and driving best practices
- Strong business acumen and the ability to translate complex data-driven insights into actionable strategies that create significant business impact
- Skilled in designing, executing, and scaling A/B or multivariate experiments to validate and optimize data science solutions
- Excellent communication and stakeholder management skills, with experience collaborating across business, product, and engineering functions
- Proven track record of driving innovation and staying current with the latest advancements in machine learning and data science
More Info
About Company
Skills:
Deep Neural Network Models (CNN, Transformers), Large Language Models (LLMs), Predictive Modeling, Sql, Tensorflow, Pytorch, Rnn, Python, Machine Learning Frameworks, Conversational AI Solutions, Classical Analytics Tools, NLP Models
Skills:
data engineering , Advanced Analytics, Advanced Excel, Sql, Predictive Modelling, Data Modelling, Python, Model governance, Explainability principles, scenario analysis, AI-driven analytics, ETL processes, Credit risk analytics, Relational Databases, Data quality best practices
Skills:
Nlp, Git, Pytorch, Docker, Statistical Analysis, Kubernetes, PEFT, pgvector, scikit-learn, Huggingface transformers, vLLM, Containerisation, LangGraph, dvc, spaCy, LoRA fine-tuning, Large Language Models
Skills:
Github, Machine Learning, Tensorflow, Numpy, Pandas, Azure Functions, MLops, Pytorch, Docker, XGBoost, Databricks, Azure Machine Learning, Python, Kubernetes, Azure DevOps, scikit-learn, LightGBM, MLflow, Azure Container Apps, Azure Monitor, Application Insights
Skills:
Logistic Regression, Decision Tree, Linear Regression, Svm, scrapy, Pca, Sql, Random Forest, Nosql, Neural Network, Pandas, Gcp, Numpy, Spark, Azure, Python, AWS, scikit-learn, K-mean Clustering, R, scapy, BERT