Data Scientist

Ambush

full-time

Posted on: 9/19/2025

Origin: • 🇺🇸 United States

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Mid-LevelSenior

Amazon RedshiftAWSBigQueryCloudJavaScriptNext.jsNumpyPandasPythonPyTorchRustScalaScikit-LearnSparkSQLTensorflow

About the role

Build, validate, and deploy statistical and machine learning models (e.g. anomaly detection, fraud detection, clustering, NLP, generative AI)
Experiment with advanced techniques (e.g. graph neural networks, LLMs, deep learning) to push the boundaries of what data can deliver
Collaborate with engineering, product, and business teams to translate strategic questions into data-driven solutions
Contribute to architectural discussions on scalable, production-ready ML systems
Share your knowledge with a team of passionate engineers and help foster a culture of agile, data-driven decision-making
Use your excellent English skills to communicate daily, both verbally and in writing, in cross-functional teams

Strong programming skills in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch) and SQL
Proven experience in Data Science, Data Analysis, or related roles
Solid foundation in statistics, data modeling, and applied machine learning
Hands-on experience with cloud platforms (AWS preferred: SageMaker, Lambda, RDS, S3)
Ability to communicate insights clearly to both technical and non-technical stakeholders
Experience with version control (Git) and modern development practices
Excellent English communication
Experience level: Senior-level
Nice to haves: Background in scientific research or advanced studies (MSc/PhD in a quantitative field)
Nice to haves: Experience with LLMs (e.g. GPT, LangChain, Whisper, vector databases) or graph neural networks
Nice to haves: Proficiency with full-stack or API development (FastAPI, Next.js, etc.)
Nice to haves: Familiarity with big data tools (Spark, BigQuery, Redshift)
Nice to haves: Exposure to fraud detection, anomaly detection, or other high-impact ML use cases
Nice to haves: Knowledge of efficient compiled languages (C++, Rust, Scala) for numerical computation