Jr. Data Scientist

Role Overview: As a Data Scientist at Tibil Solutions, you will be responsible for developing, training, and deploying machine learning models to solve business problems. You will work with structured/tabular datasets to perform data cleaning, preprocessing, and feature engineering. Additionally, you will apply statistical techniques and build regression and predictive models to extract actionable insights and communicate findings effectively to stakeholders. Key Responsibilities: - Develop, train, and deploy machine learning models to solve business problems. - Perform data cleaning, preprocessing, and feature engineering on structured datasets. - Apply statistical techniques such as hypothesis testing, probability analysis, and correlation studies. - Build and validate regression and predictive models using machine learning techniques. - Analyze large datasets to extract actionable insights. - Work closely with product, engineering, and business teams to understand requirements. - Communicate model performance, insights, and recommendations to stakeholders in a clear and concise manner. - Continuously improve models through experimentation and evaluation. Qualifications Required: - 3+ years of experience as a Data Scientist or in a similar role. - Strong expertise in classical machine learning algorithms and regression modeling. - Solid understanding of statistics including: - Probability - Statistical distributions - Hypothesis testing - Correlation analysis - Proficiency in Python and key data science libraries such as scikit-learn, pandas, NumPy. - Experience working with structured/tabular data. - Strong analytical thinking and problem-solving skills. - Ability to explain complex models and results to non-technical stakeholders. Role Overview: As a Data Scientist at Tibil Solutions, you will be responsible for developing, training, and deploying machine learning models to solve business problems. You will work with structured/tabular datasets to perform data cleaning, preprocessing, and feature engineering. Additionally, you will apply statistical techniques and build regression and predictive models to extract actionable insights and communicate findings effectively to stakeholders. Key Responsibilities: - Develop, train, and deploy machine learning models to solve business problems. - Perform data cleaning, preprocessing, and feature engineering on structured datasets. - Apply statistical techniques such as hypothesis testing, probability analysis, and correlation studies. - Build and validate regression and predictive models using machine learning techniques. - Analyze large datasets to extract actionable insights. - Work closely with product, engineering, and business teams to understand requirements. - Communicate model performance, insights, and recommendations to stakeholders in a clear and concise manner. - Continuously improve models through experimentation and evaluation. Qualifications Required: - 3+ years of experience as a Data Scientist or in a similar role. - Strong expertise in classical machine learning algorithms and regression modeling. - Solid understanding of statistics including: - Probability - Statistical distributions - Hypothesis testing - Correlation analysis - Proficiency in Python and key data science libraries such as scikit-learn, pandas, NumPy. - Experience working with structured/tabular data. - Strong analytical thinking and problem-solving skills. - Ability to explain complex models and results to non-technical stakeholders.