Job Title

Company OverviewSoftSensor AI is a data science and analytics professional services firm headquartered in New Jersey, with delivery centers in India. We build AI/ML solutions that help organizations improve performance, reduce operational cost, and create intelligent products. Our teams operate across data, modeling, and engineering to ship measurable outcomes.Role OverviewSoftSensor AI is hiring an Associate – Data Science for our Jaipur office. In this role, you will develop machine learning models, run disciplined experiments, and contribute to production-ready solutions. You will collaborate with senior data scientists and engineers to move from problem definition to model evaluation, integration, and performance measurement.Key ResponsibilitiesBuild and evaluate machine learning models (regression, classification, clustering, time series) for client and product use cases.Perform data preprocessing, feature engineering, and EDA for structured and, when required, unstructured data.Establish strong baselines and iterate systematically via error analysis, ablations, and metric-driven improvements.Design evaluation methodologies (cross-validation, holdouts, leakage checks) and select appropriate metrics (precision/recall, ROC-AUC, RMSE, etc.).Partner with engineering on deployment patterns (batch scoring, APIs), model packaging, and monitoring (performance and drift proxies).Document model assumptions, limitations, and risks; support stakeholder reviews with clear narratives and recommendations.Contribute reusable code and internal accelerators (evaluation harnesses, preprocessing utilities, templates).Maintain standards for reproducibility and quality (version control, experiment tracking, clean code practices).Required QualificationsBachelor’s/Master’s in Mathematics, Statistics, Computer Science, Engineering, or related quantitative fields.0–2 years of experience in data science or ML (internships and substantial project work considered).Strong understanding of ML fundamentals: bias/variance, overfitting, regularization, evaluation metrics, and error analysis.Proficiency in Python and common ML/data libraries (NumPy, pandas, scikit-learn).Working knowledge of SQL and data extraction from relational systems; familiarity with NoSQL is a plus.Strong analytical thinking and problem-solving ability.Strong communication skills—ability to explain results and tradeoffs to non-technical stakeholders.Preferred Qualifications (Nice to Have)Exposure to deep learning (PyTorch/TensorFlow), computer vision, NLP, or LLM/RAG workflows.Familiarity with MLOps tools (MLflow), Docker, CI/CD basics, model monitoring, or cloud ML platforms (Azure preferred).Experience working with noisy labels, imbalanced datasets, or real-world production constraints.High academic performance and/or strong portfolio (GitHub/Kaggle/projects).What Success Looks Like (First 3–6 Months)Delivered at least one model component into a real workflow (v1 in production or pilot), with measurable improvement over baseline.Demonstrated disciplined evaluation and strong documentation.Collaborated effectively with engineering and stakeholders to operationalize results.