Data Architect

Job Summary:

The Data Scientist will be responsible for collecting, analyzing, and interpreting large datasets to help drive strategic business decisions. This role involves building predictive models, applying machine learning algorithms, and delivering actionable insights that support data-driven decision-making across the organization. The ideal candidate has strong analytical skills, statistical knowledge, and experience working with modern data tools and technologies.

Key Responsibilities:

• Collect, clean, and preprocess structured and unstructured data from various sources.
  
• Develop, test, and deploy

  machine learning

  and

  statistical models

  to solve business problems.
• Analyze large datasets to uncover trends, correlations, and patterns that inform decision-making.
  
• Communicate insights and recommendations to stakeholders through clear visualizations and reports.
  
• Collaborate with cross-functional teams (engineering, product, marketing, finance, etc.) to integrate data-driven solutions.
  
• Evaluate model performance and continuously improve algorithms and data pipelines.
  
• Stay up-to-date with advancements in AI, data science methodologies, and analytics technologies.
  
• Document methodologies, models, and processes to ensure reproducibility and transparency.
  
• Assist in defining data requirements and data governance best practices.
  

Qualifications & Skills:

• Bachelors or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
  
• 3+ years of hands-on experience in data analysis, predictive modeling, or applied machine learning.
  
• Proficiency in

  Python

  or

  R

  and familiarity with relevant libraries (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, etc.).
• Strong understanding of

  statistics

  ,

  probability

  , and

  data mining techniques

  .
• Experience with

  SQL

  and

  data querying

  .
• Familiarity with

  data visualization tools

  (Tableau, Power BI, Matplotlib, Seaborn, etc.).
• Knowledge of

  big data technologies

  (Spark, Hadoop) is a plus.
• Experience with

  cloud platforms

  (AWS, GCP, or Azure) preferred.
• Strong communication skills to present technical findings to non-technical audiences.
  
• Detail-oriented, with the ability to handle multiple projects in a fast-paced environment.
  

Preferred Skills:

• Experience deploying models into production (MLOps).
  
• Familiarity with

  APIs

  ,

  ETL pipelines

  , and

  data warehousing

  (Snowflake, Redshift, BigQuery).
• Knowledge of

  NLP

  ,

  computer vision

  , or

  deep learning

  techniques.
• Exposure to

  A/B testing

  ,

  recommendation systems

  , or

  forecasting

  projects.
• Certification in data science, AI, or cloud computing (e.g., AWS Certified Data Analytics, Google Professional Data Engineer).
  

Compensation & Benefits:

• Competitive salary and performance-based incentives
  
• Health, dental, and wellness benefits
  
• Professional development and certification support
  
• Flexible working arrangements (remote/hybrid)
  
• Collaborative, data-driven culture