Job Overview
Vision Tact is looking for a Data Scientist who thrives on turning raw data into actionable insights. As part of our Data Science & AI Department, you’ll be responsible for collecting, analyzing, and interpreting large volumes of data to drive intelligent business decisions, predictive analytics, and AI-powered automation.
The role involves close collaboration with AI engineers, developers, and business strategists to design end-to-end data pipelines — from data ingestion to visualization. You’ll also work on research-driven analytics, helping clients across industries (healthcare, retail, industrial automation, geospatial intelligence, and marketing) uncover patterns that inform growth and innovation.
This position demands a strong analytical mindset, proficiency in statistical modeling, and the ability to communicate technical findings to non-technical stakeholders effectively.
Key Responsibilities
Collect, clean, and preprocess structured and unstructured data from multiple sources including databases, APIs, and external platforms.
Perform exploratory data analysis (EDA) to identify trends, anomalies, and insights using statistical and machine learning methods.
Build predictive and prescriptive models to solve business problems across domains like customer segmentation, forecasting, and anomaly detection.
Collaborate with AI/ML engineers for model integration, validation, and deployment into live systems.
Develop and maintain automated data pipelines and dashboards for continuous analytics.
Create comprehensive data visualization reports and dashboards using BI tools for internal and client stakeholders.
Work closely with domain experts and business teams to define key metrics, success indicators, and data-driven decision frameworks.
Implement robust data governance and validation practices to ensure data quality and consistency.
Stay updated on evolving data science techniques, cloud-based analytics tools, and AI trends to continuously enhance Vision Tact’s analytical capabilities.
Required Skills & Tools
Programming: Python (primary), R, SQL, and experience with Jupyter Notebooks.
Libraries: NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch (for applied ML), Statsmodels.
Visualization Tools: Power BI, Tableau, Plotly, Matplotlib, or Seaborn.
Databases: MySQL, PostgreSQL, MongoDB, BigQuery, or similar.
Cloud & Platforms: AWS, Azure, or Google Cloud for analytics and data pipeline management.
Statistical Techniques: Regression, classification, clustering, time-series forecasting, A/B testing.
Soft Skills: Data storytelling, stakeholder communication, and translating data insights into business actions.
Experience with LLMs or generative AI data preparation will be considered an added advantage.
Qualifications
Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or a related quantitative field.
Minimum 3 years of experience in applied data science, analytics, or related roles.
Strong portfolio of analytical projects (GitHub, Kaggle, or professional work samples preferred).
Certifications such as Google Data Analytics Professional, Microsoft Certified: Data Scientist Associate, or AWS Data Analytics Specialty are desirable.
Experience working with cross-functional AI or software development teams is an asset.
