Data Scientist

Key Responsibilities

• 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.

Required Skills and Qualifications

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.