Senior Specialist - Data Sciences

Mandatory skill - Python, SQL, Time Series Forecasting, Statistical Analysis, Data Science JD: Resultsdriven Data Scientist with handson experience in timeseries forecasting statistical analysis data analysis data wrangling and predictive modeling using Python and SQL Strong understanding of forecasting techniques trends seasonality model evaluation and forecast accuracy improvement Adept at translating complex datasets and forecasting outputs into actionable business recommendations Key Skill Areas Strong understanding of timeseries concepts including trend seasonality cyclic patterns lag features rolling statistics anomalies and historical patterns Handson knowledge of forecasting models such as Prophet ARIMASARIMA MSTLARIMA TBATS Exponential SmoothingHoltWinters and Theta models Descriptive and inferential statistics probability distributions correlation and covariance regression analysis hypothesis testing confidence intervals variance analysis statistical significance outlier detection and trend analysis Forecast Model Evaluation Optimization Forecast accuracy evaluation using MAPE and percentageerrorbased metrics timebased validation model comparison hyperparameter tuning overfittingunderfitting analysis forecast bias identification Exploratory Data Analysis EDA trend and correlation analysis historical pattern analysis missingvalue treatment outlier handling lagrolling features holiday and event features external regressorscovariates and dailyweeklymonthly data analysis Soft Skills Strong problemsolving ability attention to detail stakeholder communication ability to translate business problems into data solutions Must Have Developed maintained and evaluated timeseries forecasting models to predict business performance using historical actuals trends seasonality and statistical patterns Applied forecasting techniques such as Prophet ARIMASARIMA MSTLARIMA TBATS and other statistical forecasting approaches to solve business forecasting problems 
 Performed forecast accuracy analysis model comparison and hyperparameter tuning to improve forecasting performance 
 Conducted indepth analysis of historical data to identify trends seasonality anomalies forecast deviations and key business drivers 
 Performed forecast vs actual analysis and rootcause analysis to understand significant deviations and recommend model or data improvements 
 Used Python and SQL for data preparation and transformation feature 
 Incorporated holidays events and business covariates to improve forecasting models 
 Collaborated with stakeholders to gather forecasting requirements translate business questions into analytical approaches and communicate model results and forecast insights clearly 
 Good To Have 
 Familiarity with prompt engineering CopilotOpenAI APIs and basic usage of GPTLLM models for analytical workflow automation Awareness of LLM limitations responsible AI practices and applications of GenAI in analytics Familiarity with advanced forecastingML approaches such as XGBoost Neural Prophet NBEATS or ensemble forecasting Experience with Microsoft Fabric Fabric Notebooks Lakehouse and Pipelinesfor running and operationalizing data scienceforecasting workloads Willing to work in shifts