Nectar is an AI-first product company building intelligent platforms that transform how enterprises leverage AI, automation, and connected technologies. Our products combine Generative AI, Agentic AI, IoT, and cloud-native technologies to deliver intelligent, scalable, and secure experiences for customers worldwide.
At Nectar, we continuously innovate and enhance our core product platform, enabling organizations to unlock actionable insights, automate operations, and make data-driven decisions in real time.
As a Data Scientist at Nectar, you will help build intelligent capabilities that power our core product platform. You will work with large volumes of IoT, operational, and enterprise data to develop predictive models, generate actionable insights, and create data-driven features that directly enhance customer experiences.
You will collaborate closely with AI engineers, data engineers, product managers, and domain experts to transform complex data into scalable product capabilities. This role offers an exciting opportunity to work at the intersection of Data Science, Artificial Intelligence, IoT, and real-time analytics.
- Analyze large volumes of structured and unstructured data to identify patterns, trends, and business insights.
- Develop, validate, and deploy machine learning models for prediction, classification, anomaly detection, and optimization use cases.
- Build data-driven features that enhance Nectar's core product platform.
- Work with IoT and operational datasets to generate predictive insights and intelligent recommendations.
- Perform exploratory data analysis (EDA) to uncover opportunities for product improvements.
- Collaborate with AI Engineers and Data Engineers to productionize machine learning models.
- Design and evaluate experiments, model performance metrics, and statistical analyses.
- Develop dashboards, reports, and visualizations to communicate insights to technical and business stakeholders.
- Monitor model performance and continuously improve accuracy, reliability, and business impact.
- Ensure data quality, reproducibility, and best practices throughout the data science lifecycle.
- Stay up to date with advancements in Machine Learning, Generative AI, and applied data science.
RequirementsWhat You Bring
Required Experience
- 1–3 years of hands-on experience in Data Science, Machine Learning, Analytics, or related roles.
- Strong programming skills in Python and SQL.
- Experience building and evaluating machine learning models using real-world datasets.
- Strong understanding of statistics, probability, and machine learning fundamentals.
- Experience with data analysis, feature engineering, and model evaluation techniques.
- Familiarity with supervised and unsupervised learning algorithms.
- Experience working with large datasets and data preprocessing techniques.
- Understanding of data visualization and storytelling principles.
- Basic understanding of cloud platforms such as AWS, Azure, or GCP.
- Strong analytical thinking and problem-solving abilities.
- Good communication and collaboration skills with the ability to work in cross-functional product teams.
- Programming Languages: Python, SQL
- Machine Learning: Regression, Classification, Clustering, Time Series Forecasting, Anomaly Detection
- Libraries & Frameworks: Scikit-learn, Pandas, NumPy, XGBoost, TensorFlow, PyTorch
- Data Analysis & Visualization: Matplotlib, Plotly, Power BI, Tableau
- Statistics & Experimentation: Hypothesis Testing, A/B Testing, Statistical Modeling
- Data Processing: Pandas, PySpark, Feature Engineering
- Databases: PostgreSQL, MySQL, MongoDB
- Cloud Platforms: AWS, Azure, or GCP
- MLOps & Deployment: MLflow, Docker, Git, CI/CD (preferred)
- Generative AI (Preferred): LLM Fundamentals, Prompt Engineering, Embeddings
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
- Relevant certifications or hands-on project experience in Machine Learning, Data Science, or AI will be an added advantage.
- Experience working with IoT, telemetry, or sensor-generated datasets.
- Familiarity with time-series analysis and forecasting techniques.
- Exposure to Generative AI, LLMs, or Retrieval-Augmented Generation (RAG) concepts.
- Experience deploying machine learning models into production environments.
- Contributions to open-source projects, technical blogs, Kaggle competitions, or research initiatives are highly desirable.
BenefitsWhy You'll Love Working at Nectar
- Build intelligent features that directly enhance Nectar's AI-powered platform.
- Work on challenging problems involving Machine Learning, AI, IoT, and real-time analytics.
- Collaborate with a passionate and innovative product engineering team.
- Gain end-to-end exposure from data exploration and model development to deployment and continuous improvement.
- Continuous learning opportunities, certifications, and career growth in Data Science and AI.
- Work in a fast-paced environment where your contributions have visible and lasting impact.
Skills Required
- 1-3 years of hands-on experience in Data Science, Machine Learning, Analytics, or related roles
- Strong programming skills in Python and SQL
- Experience building and evaluating machine learning models using real-world datasets
- Strong understanding of statistics, probability, and machine learning fundamentals
- Experience with data analysis, feature engineering, and model evaluation techniques
- Familiarity with supervised and unsupervised learning algorithms
- Experience working with large datasets and data preprocessing techniques
- Understanding of data visualization and storytelling principles
- Basic understanding of AWS, Azure, or GCP
- Strong analytical thinking and problem-solving abilities
- Good communication and collaboration skills in cross-functional product teams
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field
- Relevant certifications or hands-on project experience in Machine Learning, Data Science, or AI
- Experience working with IoT, telemetry, or sensor-generated datasets
- Familiarity with time-series analysis and forecasting techniques
- Exposure to Generative AI, LLMs, or Retrieval-Augmented Generation concepts
- Experience deploying machine learning models into production environments
- Contributions to open-source projects, technical blogs, Kaggle competitions, or research initiatives
- Experience with MLflow, Docker, Git, and CI/CD
- Knowledge of LLM fundamentals, prompt engineering, and embeddings
What We Do
Nectir AI provides AI infrastructure for classrooms and campuses, enabling educators to create customizable, FERPA-compliant AI assistants within learning management systems. Its platform supports safe, secure interactions between educators and students and offers 24/7 personalized learning assistance grounded in course content. The company emphasizes academic integrity, privacy, and compliance, including FERPA and SOC 2 standards for institutions seeking trusted educational AI deployment.
