Data Science & Machine Learning Specialist

Job Description

Role Overview:You should have 5 to 9 years of hands-on experience in data science projects, with a preference for experience across different domains. Your role will require strong statistical expertise, including skills in hypothesis testing, linear and non-linear regression, classification techniques, and probability distributions. Your proven ability to translate complex business problems into data science use cases with practical impact will be crucial. Additionally, you should be experienced in building and validating machine learning models, encompassing classification, regression, and survival analysis. Proficiency in Python is essential, including knowledge of libraries like Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn, as well as SQL for data querying and analysis. Experience in handling both structured and unstructured datasets, expertise in exploratory data analysis (EDA), and data cleaning will be important for this role. Strong communication skills are necessary, as you will need to explain technical concepts clearly to non-technical stakeholders. Familiarity with version control tools such as Git/GitHub and collaborative development workflows is expected. You should also possess a deployment mindset, with an understanding of how to build data products that are usable, scalable, and maintainable.Key Responsibilities:- Utilize your statistical expertise to perform hypothesis testing, regression analysis, and classification techniques.- Translate complex business problems into data science use cases with practical impact.- Build and validate machine learning models, including classification, regression, and survival analysis.- Use Python and relevant libraries for data analysis and modeling, along with SQL for data querying.- Handle both structured and unstructured datasets, focusing on exploratory data analysis (EDA) and data cleaning.- Communicate technical concepts clearly to non-technical stakeholders.- Collaborate with teams using version control tools such as Git/GitHub for efficient workflow.- Develop data products that are usable, scalable, and maintainable.Qualifications Required:- 5 to 9 years of hands-on experience in data science projects.- Strong statistical expertise in hypothesis testing, regression, classification techniques, and probability distributions.- Proficiency in Python, including libraries like Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn.- Experience in handling both structured and unstructured datasets, with expertise in exploratory data analysis (EDA) and data cleaning.- Strong communication skills for explaining technical concepts to non-technical stakeholders.- Familiarity with version control tools such as Git/GitHub and collaborative development workflows.- Deployment mindset with the ability to build usable, scalable, and maintainable data products. Role Overview:You should have 5 to 9 years of hands-on experience in data science projects, with a preference for experience across different domains. Your role will require strong statistical expertise, including skills in hypothesis testing, linear and non-linear regression, classification techniques, and probability distributions. Your proven ability to translate complex business problems into data science use cases with practical impact will be crucial. Additionally, you should be experienced in building and validating machine learning models, encompassing classification, regression, and survival analysis. Proficiency in Python is essential, including knowledge of libraries like Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn, as well as SQL for data querying and analysis. Experience in handling both structured and unstructured datasets, expertise in exploratory data analysis (EDA), and data cleaning will be important for this role. Strong communication skills are necessary, as you will need to explain technical concepts clearly to non-technical stakeholders. Familiarity with version control tools such as Git/GitHub and collaborative development workflows is expected. You should also possess a deployment mindset, with an understanding of how to build data products that are usable, scalable, and maintainable.Key Responsibilities:- Utilize your statistical expertise to perform hypothesis testing, regression analysis, and classification techniques.- Translate complex business problems into data science use cases with practical impact.- Build and validate machine learning models, including classification, regression, and survival analysis.- Use Python and relevant libraries for data analysis and modeling, along with SQL for data querying.- Handle both structured and unstructured datasets, focusing on exploratory data analysis (EDA) and data cleaning.- Communicate technical concepts clearly to non-technical stakeholders.- Collaborate with teams using version control tools such as Git/GitHub for efficient workflow.- Develop data products that are usable, scalable, and

Keyskills :
PythonNumPyMatplotlibSQLMachine LearningData ScienceStatistical AnalysisHypothesis TestingRegressionExploratory Data AnalysisData CleaningCommunication SkillsVersion ControlGitGitHubPandasScikitlearnSeabornClassification Techniques