Job Details

Job Summary:

We are looking for a highly analytical and innovative Data Scientist to join our team. The ideal candidate will be responsible for collecting, analyzing, and interpreting large datasets to derive actionable insights that support business decisions. You will work closely with cross-functional teams to develop data-driven solutions, build predictive models, and contribute to the company’s data strategy.

Key Responsibilities:

• Data Analysis & Modeling
  Analyze structured and unstructured data using statistical methods and machine learning techniques
  Build, validate, and deploy predictive and prescriptive models

• Data Collection & Management
  Collect data from various sources, including internal databases, APIs, and third-party platforms
  Clean, preprocess, and transform data to ensure quality and usability

• Insight Generation
  Translate complex data sets into clear insights and visualizations for stakeholders
  Communicate findings through reports, dashboards, and presentations

• Collaboration & Strategy
  Work with engineering, marketing, product, and business teams to align data initiatives with company goals
  Identify opportunities for leveraging data to drive product or business innovation

• Tool & Technology Use
  Utilize programming languages and tools (Python, R, SQL, Tableau, etc.) for data analysis and visualization
  Automate repetitive data tasks and processes when possible

Qualifications:

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field
• 2–5 years of experience as a Data Scientist or in a similar analytical role

Preferred Skills:

• Proficient in Python, R, SQL, and data visualization tools like Tableau, Power BI, or Looker
• Experience with machine learning frameworks (e.g., scikit-learn, TensorFlow, Keras)
• Strong statistical analysis and modeling skills
• Familiarity with big data platforms like Spark, Hadoop, or cloud services (AWS, Azure, GCP)
• Excellent communication and storytelling skills to explain data-driven findings to non-technical stakeholders

Why Join Us:

• Work in a data-driven and innovative environment
• Collaborate with talented teams across disciplines
• Enjoy opportunities for professional development and upskilling
• Competitive salary and comprehensive benefits package

• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field (required)
• Master’s degree or PhD (preferred, especially for senior or research-heavy roles)

Experience:

• 2–5 years of hands-on experience in data science, analytics, or machine learning
• Proven track record of building and deploying data models, algorithms, or predictive analytics solutions
• Experience working with large and complex datasets from multiple sources

Technical Skills:

• Proficient in programming languages such as Python, R, or Scala
• Strong understanding of machine learning algorithms, statistical modeling, and data mining techniques
• Solid skills in SQL and working with relational or NoSQL databases
• Familiarity with data visualization tools (e.g., Tableau, Power BI, Looker, matplotlib, seaborn)
• Experience with big data tools (e.g., Hadoop, Spark) and cloud platforms (AWS, GCP, or Azure) is a plus
• Knowledge of ETL pipelines, data wrangling, and APIs for data integration

Analytical & Problem-Solving Skills:

• Strong statistical analysis and critical thinking capabilities
• Ability to interpret complex data sets and translate them into meaningful insights
• Comfortable working with ambiguity and solving open-ended problems

Soft Skills:

• Excellent communication and storytelling skills for both technical and non-technical audiences
• Strong collaboration skills to work with cross-functional teams (engineering, marketing, product, etc.)
• Self-motivated with a passion for continuous learning and staying current on new tools and trends
• Detail-oriented with a strong focus on data accuracy and reliability

Additional Requirements:

• Ability to manage multiple projects and deadlines in a fast-paced environment
• Familiarity with A/B testing, model evaluation metrics, and data ethics
• Publications, GitHub contributions, or participation in data competitions (like Kaggle) is a plus