Job Description
Acceldata is reimagining the way companies observe their Data!
Acceldata is the pioneer and leader in data observability, revolutionizing how enterprises manage and observe data by offering comprehensive insights into various key aspects of data, data pipelines and data infrastructure across various environments. Our platform empowers data teams to manage products effectively by ensuring data quality, preventing failures, and controlling costs.
As a Data Scientist
You will play a crucial role in analysing complex datasets, developing machine learning models, and deriving actionable insights to drive business decisions. You will work closely with senior data scientists and cross-functional teams to solve challenging problems and contribute to the advancement of data-driven initiatives.
A day in the life of Data Scientist
Collect, clean, and preprocess large datasets from various sources to ensure data quality and integrity.
Perform exploratory data analysis to identify patterns, trends, and anomalies.
Develop and implement machine learning models for predictive analytics, classification, clustering, and regression tasks.
Evaluate model performance using appropriate metrics and techniques, and iterate on model improvements.
Collaborate with software engineers to deploy models into production environments and integrate them into scalable systems.
Utilize statistical techniques and methodologies to derive actionable insights and recommendations.
Communicate findings and results to technical and non-technical stakeholders through reports, visualizations, and presentations.
Stay updated on the latest advancements in data science, machine learning, and related technologies.
Contribute to the development and maintenance of data pipelines, tools, and infrastructure.
You are a great fit for this role if you have
Bachelors or Masters degree in Computer Science, Statistics, Mathematics, Engineering, or related field.
Strong proficiency in programming languages such as Python or R.
Experience with data manipulation and analysis libraries such as Pandas, NumPy, and scikit-learn.
Knowledge of machine learning algorithms and frameworks, and practical experience in model development and evaluation.
Familiarity with database systems and SQL for data extraction and manipulation.
Excellent analytical and problem-solving skills, with attention to detail.
Strong communication and interpersonal skills, with the ability to collaborate effectively in a team environment.
