Data Scientist (Hybrid)

• Problem 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 performance.

• Communication 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.

Technical Skills:

• Programming Languages: Proficiency in at least one or more data-centric languages, such as Python (most common, with libraries like NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch) and R (popular for 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.