Data Scientist

OverviewDirect message the job poster from Kaizen TechnologiesResponsibilitiesProblem Identification: Work with business stakeholders (e.g., marketing, finance, product) to understand business challenges and identify opportunities where data science can provide a solution.Data Collection & Management: Identify, collect, and organize large, complex, and sometimes unstructured datasets from various sources (e.g., internal databases, APIs, web scraping).Data Wrangling and Cleaning: Clean, preprocess, and transform raw data into a usable format. This is often a time-consuming but critical part of the job to ensure data quality and accuracy.Exploratory Data Analysis (EDA): Perform in-depth analysis of the data to uncover patterns, trends, and relationships. This involves using statistical methods and data visualization tools.Model Development: Design, build, train, and test machine learning models and algorithms (e.g., for classification, regression, clustering, forecasting).Model Deployment: Work with data engineers and software developers to deploy models into production environments and monitor their munication and Storytelling: Translate complex technical findings into clear, actionable business insights. This often involves creating compelling reports, presentations, and interactive dashboards for a non-technical audience.Continuous Improvement: Stay up-to-date with emerging data science technologies, methods, and tools. Continuously refine models and analytical processes to improve efficiency and accuracy.Required Skills and QualificationsTechnical Skills:Programming Languages: Proficiency in at least one or more data-centric languages, such as Python (libraries like NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch) and R (statistical analysis).Database Management: Strong knowledge of SQL for querying and managing databases. Experience with NoSQL databases may also be required.Statistics and Mathematics: A solid foundation in statistical concepts, including probability, hypothesis testing, regression analysis, and experimental design (e.g., A/B testing).Machine Learning: A deep understanding of machine learning algorithms and techniques, including supervised and unsupervised learning, and model evaluation metrics.Data Visualization: Experience with data visualization tools like Tableau, Power BI, Matplotlib, Seaborn, or D3.js to create charts, dashboards, and reports.Big Data Technologies: Familiarity with big data tools and frameworks like Apache Spark, Hadoop, and cloud platforms (e.g., AWS, Azure) is increasingly important.Seniority levelMid-Senior levelEmployment typeFull-timeJob functionInformation TechnologyIndustriesSoftware DevelopmentReferrals increase your chances of interviewing at Kaizen Technologies by 2xData Scientist (Hybrid) - Permanent job role listings and location postings shown are part of the original content and have been trimmed to maintain focus on the role. #J-18808-Ljbffr

Created: 2025-09-24