Machine Learning Engineer/Analyst

Introduction

We are looking for an analytical and detail-oriented Machine Learning Analyst to join our team. In this role, you will be building scalable machine learning applications and will bridge the gap between technical data science work and actionable business strategy. You will be responsible for cleaning and preparing data, building baseline models, analyzing model performance, and most importantly translating complex model outputs into clear insights for stakeholders.

If you love digging into data to understand why a model made a specific prediction and enjoy storytelling with data, this role is for you. You will be highly encouraged to explore new technologies and come up with innovative solutions that could take the team’s capabilities to the next level.

Job Responsibilities

• Data Preparation: Extract, clean, and organize large datasets using SQL and Python to get them ready for analysis.
• Model Building: develop, test, and maintain machine learning models to predict business outcomes.
• Contextualize Performance: Move beyond technical metrics (like accuracy or error rates) to explain model results in terms of ROI, customer behavior, and risk.
• Present the "So What?": Craft executive presentations (using PowerPoint, etc.) that explain why a model prediction matters and how it impacts business strategy.
• Visual Storytelling: Design intuitive charts and dashboards that guide the audience through the data, highlighting the most critical trends and insights.
• Advise & Influence: Act as a data consultant, using your analysis to persuade team leaders and drive evidence-based decision-making.
• End-to-End Analysis: Manage the full narrative arc of a project, from cleaning the raw data to delivering the final recommendation.
• Collaboration: Work closely with engineers and managers to identify new opportunities where data can solve problems.

Work Hours: 2 PM - 10 PM, (Monday to Friday)

Knowledge and Experience

• 3+ years of hands-on experience in building machine learning models and pipelines.
• Working knowledge of one or more SQL languages: Snowflake, PostgreSQL, Oracle, MySQL, etc.
• Working knowledge of one or more scientific programming languages/packages: Python, Java, etc.
• Knowledge and solid understanding of fundamental Machine Learning concepts (Regression, Classification, Forecasting) and algorithms.
• Data Storytelling: Ability to weave complex data and model outputs into a clear, compelling narrative that drives business action.
• Experience with visualization tools like Power BI, Tableau, and Python libraries (Matplotlib, Seaborn, Plotly).
• Familiarity with version control (Git).

Preferred Skills (Nice to Haves):

• Experience with cloud platforms (AWS, Google Cloud, Azure).
• Experience with LLMs, Streamlit
• Knowledge of NLP (Natural Language Processing) or basic Deep Learning frameworks (PyTorch/TensorFlow).

Tech Stack:

• Languages: Python, SQL (Snowflake & PostgreSQL)
• Libraries: Scikit-learn, Pandas, XGBoost, LightGBM, TensorFlow etc.
• Reporting and presentation: Excel, PowerPoint
• Data Viz: PowerBi, Plotly, MatPlotlib
• Cloud: AWS, GCP

Education

Bachelor's or master’s degree in Computer Science, Mathematics, Statistics, Physics, or a related field with project experience in machine learning and statistical modeling.

Competencies