Discover opportunities in data science, machine learning, AI, and data analytics. Whether you work with Python, R, TensorFlow, or big data platforms, find roles that match your expertise.
19 open positions
We list Data Scientist, Data Analyst, ML Engineer, AI Researcher, Data Engineer, and Analytics Manager positions.
Common requirements include Python/R, SQL, machine learning frameworks (TensorFlow, PyTorch), statistics, and cloud platforms (AWS, GCP).
Data scientists in the US earn $95k–$160k on average, with senior data scientists and ML engineers earning $160k–$250k+.
Data analysts focus on querying, cleaning, and visualizing existing data to generate business insights — primarily using SQL, Excel, and BI tools like Tableau or Looker. Data scientists go deeper: they build predictive models, work with machine learning, and often write production-level Python or R code. The line is blurring, but analysts are more business-focused while scientists are more research and engineering-oriented.
PyTorch has overtaken TensorFlow as the leading deep learning framework, especially in research. For production ML, scikit-learn, XGBoost, and LightGBM are workhorses for tabular data. For LLM applications, LangChain, Hugging Face Transformers, and OpenAI's API are key. MLflow and Weights & Biases are standard for experiment tracking.
No. Most industry data science roles require a bachelor's or master's degree in a quantitative field (math, stats, CS, engineering). A PhD is typically only needed for pure research positions at companies like Google DeepMind, OpenAI, or academic labs. Industry roles prioritize hands-on skills and portfolio projects over credentials.
Entry-level: Junior Data Scientist or Data Analyst. Mid-level: Data Scientist or Senior Analyst. Senior: Senior Data Scientist or Staff Scientist. Leadership: Principal Scientist, ML Lead, or Head of Data Science / Director of Analytics. Many data scientists also move into ML Engineering or AI Product roles.
AI tools (Copilot, ChatGPT) are automating routine data analysis tasks, raising the bar for what data scientists are expected to produce. The most in-demand skills have shifted toward LLM fine-tuning, RAG (retrieval-augmented generation), MLOps, and productionizing AI systems. Data scientists who understand both the business context and the AI/ML engineering stack are the most valuable.