Job Opportunities API

About CodeNinja CodeNinja is a full-stack AI delivery company that helps enterprises, governments, and software acquirers build and operate intelligence-driven systems for mission-critical workflows. We specialize in deploying AI into real operations—combining strong engineering fundamentals with AI-native delivery to create measurable value, resilience, and long-term ownership for our clients. Our global footprint and delivery model are supported by AI Labs, AI Pods, and Global Capability Centers, enabling teams to co-engineer scalable platforms across regions and time zones. Role Overview We are seeking a Senior Data Scientist with 4+ years of experience to design, develop, and deploy advanced machine learning and forecasting solutions for high-impact business use cases. This role requires strong expertise in predictive analytics, time-series forecasting, statistical modeling, and the ability to translate complex business problems into scalable data-driven solutions. You will play a key role in building robust, interpretable, and production-ready models that support forecasting, optimization, risk analysis, and strategic decision-making across multiple domains. Requirements Machine Learning & Forecasting Design, develop, and deploy machine learning models for forecasting, prediction, and optimization use cases. Build and evaluate time-series forecasting solutions using statistical and machine learning approaches. Perform feature engineering including lag variables, rolling statistics, trend decomposition, seasonality analysis, and exogenous variable integration. Develop and benchmark models using techniques such as ARIMA, SARIMA, Prophet, XGBoost, Random Forest, Gradient Boosting, and Neural Networks. Conduct model validation, backtesting, and performance monitoring using appropriate evaluation metrics. Data Science & Analytics Analyze large and complex datasets to identify trends, anomalies, and business opportunities. Perform exploratory data analysis (EDA) and statistical investigations. Design experiments and evaluate model performance using sound statistical methodologies. Build business-focused insights and recommendations from analytical findings. Production & Deployment Collaborate with engineering teams to deploy machine learning solutions into production environments. Build scalable data pipelines and automated workflows. Implement model monitoring, drift detection, retraining strategies, and version control. Develop dashboards and reporting solutions using tools such as Power BI, Tableau, or Streamlit. Collaboration & Leadership Partner with business, product, and engineering stakeholders to define data-driven solutions. Communicate technical findings to both technical and non-technical audiences. Mentor junior team members and contribute to data science best practices. Participate in architecture discussions and support project delivery initiatives. Requirements 4+ years of hands-on experience in Data Science, Machine Learning, or Predictive Analytics. Strong experience with time-series forecasting and predictive modeling. Proficiency in Python and libraries such as Pandas, NumPy, Scikit-learn, and XGBoost. Experience with forecasting techniques such as ARIMA, SARIMA, Prophet, or similar methods. Strong understanding of statistics, hypothesis testing, feature engineering, and model evaluation. Experience with model validation, cross-validation, and performance metrics (MAPE, MAE, RMSE). Experience working with SQL and large-scale datasets. Familiarity with data pipelines, experimentation frameworks, and analytical environments such as Jupyter or Databricks. Strong communication skills with the ability to present technical insights to business stakeholders. Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field. Preferred/Advance Skills Experience in multi-entity and mu