Resume Checker for Data Scientists & ML Engineers

Data science roles attract hundreds of applicants. Companies rely on ATS to screen for specific tools, languages, and methodologies before a hiring manager ever reviews your resume. Common filtering criteria include: • Programming languages (Python, R, SQL, Scala) • ML frameworks (TensorFlow, PyTorch, scikit-learn, Keras) • Visualization tools (Tableau, Power BI, Matplotlib) • Big data tools (Spark, Hadoop, Airflow) • Statistical methods and modeling techniques

Our AI analyzes your resume for critical data science keywords: • Machine Learning, Deep Learning, NLP, Computer Vision • A/B Testing, Statistical Modeling, Hypothesis Testing • ETL, Data Pipelines, Feature Engineering • AWS SageMaker, Google Cloud AI, Azure ML • Pandas, NumPy, scikit-learn, XGBoost • SQL, NoSQL, Data Warehousing • Jupyter Notebooks, Git, Docker

1. Lead with a targeted summary mentioning your specialization (NLP, CV, recommender systems) 2. Quantify impact: "Built ML model that increased click-through rate by 23%" 3. List specific tools and frameworks — ATS matches exact names 4. Include publications, Kaggle competitions, or open-source contributions 5. Mention deployment experience (MLOps, model serving, monitoring) 6. Keep it concise (1-2 pages) with clear section headings

• Listing only tools without business context or impact • Not tailoring keywords to the specific job description • Using vague descriptions like "worked with data" instead of specific techniques • Forgetting end-to-end experience (data collection → modeling → deployment) • Missing soft skills like stakeholder communication and cross-functional collaboration • Not mentioning the size/scale of datasets you worked with