Data Scientist

Data Scientist

Key Responsibilities:

• Apply advanced

  statistical methods

   to analyze large and complex datasets and extract meaningful insights.
• Design and implement

  time series analysis

   techniques for

  forecasting

   and trend detection, leveraging models such as

  ARIMA, SARIMA.

• Develop, test, and deploy

  machine learning models

   for pattern detection, anomaly identification, and predictive analytics.
• Create scalable forecasting models to address real-world business problems across various domains.
• Perform

  hypothesis testing

   and statistical validation to ensure reliability and accuracy of findings.
• Clean, preprocess, and validate data to improve its usability and integrity for analysis.
• Visualize and interpret analytical findings to communicate actionable insights to non-technical stakeholders effectively.
• Collaborate with cross-functional teams to implement data-driven solutions tailored to business challenges.
• Continuously research and implement the latest advancements in

  statistics

  ,

  AI

  , and

  time series modeling

   to enhance analytics capabilities.

Required Qualifications:

• Bachelor’s or Master’s degree

   in

  Statistics

  ,

  Applied Mathematics

  , or a related quantitative field.

• 4 years of experience

   in data science, with a strong focus on

  statistical analysis

  ,

  time series modeling

  , and

  NLP.

• Expertise in

  time series analysis

  , including

  ARIMA

  ,

  SARIMA

  ,

  Holt-Winters

  , and

  other forecasting techniques

  .
• Proficiency in programming languages like

  Python

  ,

  R

  , or similar, with experience using libraries such as

  statsmodels

  ,

  Prophet

  ,

  Scikit-Learn

  , and

  NumPy

  .
• Strong understanding of

  statistical concepts

  , including

  regression analysis

  ,

  Bayesian methods

  , and

  hypothesis testing

  .
• Experience with data visualization tools such as

  Tableau

  ,

  PowerBI

  ,

  Matplotlib

  , or

  Seaborn

   for presenting insights.
• Hands-on experience with

  data manipulation tools

   such as

  SQL

  ,

  Pandas

  , and

  Excel

  .
• Excellent problem-solving and critical-thinking skills, with the ability to simplify complex statistical concepts for non-technical audiences.