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About Moative

Moative, an Applied AI company, designs and builds transformation AI solutions for traditional industries in energy, utilities, healthcare & lifesciences, and more. Through Moative Labs, we build AI micro-products and launch AI startups with partners in vertical markets that align with our theses.

Our Past: We have built and sold two companies, one of which was an AI company. Our founders and leaders are Math PhDs, Ivy League University Alumni, Ex-Googlers, and successful entrepreneurs.

Our Team: Our team of 20+ employees consist of data scientists, AI/ML Engineers, and mathematicians from top engineering and research institutes such as IITs, CERN, IISc, UZH, Ph.Ds. Our team includes academicians, IBM Research Fellows, and former founders.

Work you’ll do

As a Data Scientist at Moative, you’ll play a crucial role in extracting valuable insights from data to drive informed decision-making. You’ll work closely with cross-functional teams to build predictive models and develop solutions to complex business problems. You will also be involved in conducting experiments, building POCs and prototypes.

Responsibilities

• Support end-to-end development and deployment of ML/ AI models - from data preparation, data analysis and feature engineering to model development, validation and deployment
• Gather, prepare and analyze data, write code to develop and validate models, and continuously monitor and update them as needed.
• Collaborate with domain experts, engineers, and stakeholders in translating business problems into data-driven solutions
• Document methodologies and results, present findings and communicate insights to non-technical audiences

Skills & Requirements

• Proficiency in Python and familiarity with basic Python libraries for data analysis and ML algorithms (such as NumPy, Pandas, ScikitLearn, NLTK). 
• Strong understanding and experience with data analysis, statistical and mathematical concepts and ML algorithms 
• Working knowledge of cloud platforms (e.g., AWS, Azure, GCP).
• Broad understanding of data structures and data engineering.
• Strong communication skills
• Strong collaboration skills, continuous learning attitude and a problem solving mind-set

Working at Moative

Moative is a young company, but we believe strongly in thinking long-term, while acting with urgency. Our ethos is rooted in innovation, efficiency and high-quality outcomes. We believe the future of work is AI-augmented and boundary less. Here are some of our guiding principles:

• Think in decades. Act in hours. As an independent company, our moat is time. While our decisions are for the long-term horizon, our execution will be fast – measured in hours and days, not weeks and months.
• Own the canvas. Throw yourself in to build, fix or improve – anything that isn’t done right, irrespective of who did it. Be selfish about improving across the organization – because once the rot sets in, we waste years in surgery and recovery.
• Use data or don’t use data. Use data where you ought to but not as a ‘cover-my-back’ political tool. Be capable of making decisions with partial or limited data. Get better at intuition and pattern-matching. Whichever way you go, be mostly right about it.
• Avoid work about work. Process creeps on purpose, unless we constantly question it. We are deliberate about committing to rituals that take time away from the actual work. We truly believe that a meeting that could be an email, should be an email and you don’t need a person with the highest title to say that loud.
• High revenue per person. We work backwards from this metric. Our default is to automate instead of hiring. We multi-skill our people to own more outcomes than hiring someone who has less to do. We don’t like squatting and hoarding that comes in the form of hiring for growth. High revenue per person comes from high quality work from everyone. We demand it.

If this role and our work is of interest to you, please apply here. We encourage you to apply even if you believe you do not meet all the requirements listed above.  

That said, you should demonstrate that you are in the 90th percentile or above. This may mean that you have studied in top-notch institutions, won competitions that are intellectually demanding, built something of your own, or rated as an outstanding performer by your current or previous employers. 

The position is based out of Chennai. Our work currently involves significant in-person collaboration and we expect you to be present in the city. We intend to move to a hybrid model in a few months time.

Job Title : Senior Machine Learning Engineer

Experience : 8+ Years

Location : Chennai

Notice Period : Immediate Joiners Only

Work Mode : Hybrid

Job Summary :

We are seeking an experienced Machine Learning Engineer with a strong background in Python, ML algorithms, and data-driven development.

The ideal candidate should have hands-on experience with popular ML frameworks and tools, solid understanding of clustering and classification techniques, and be comfortable working in Unix-based environments with Agile teams.

Mandatory Skills :

• Programming Languages : Python
• Machine Learning : Strong experience with ML algorithms, models, and libraries such as Scikit-learn, TensorFlow, and PyTorch
• ML Concepts : Proficiency in supervised and unsupervised learning, including techniques such as K-Means, DBSCAN, and Fuzzy Clustering
• Operating Systems : RHEL or any Unix-based OS
• Databases : Oracle or any relational database
• Version Control : Git
• Development Methodologies : Agile

Desired Skills :

• Experience with issue tracking tools such as Azure DevOps or JIRA.
• Understanding of data science concepts.
• Familiarity with Big Data algorithms, models, and libraries.

We are looking for an outstanding ML Architect (Deployments) with expertise in deploying Machine Learning solutions/models into production and scaling them to serve millions of customers. A candidate with an adaptable and productive working style which fits in a fast-moving environment.

Skills:

- 5+ years deploying Machine Learning pipelines in large enterprise production systems.

- Experience developing end to end ML solutions from business hypothesis to deployment / understanding the entirety of the ML development life cycle.
- Expert in modern software development practices; solid experience using source control management (CI/CD).
- Proficient in designing relevant architecture / microservices to fulfil application integration, model monitoring, training / re-training, model management, model deployment, model experimentation/development, alert mechanisms.
- Experience with public cloud platforms (Azure, AWS, GCP).
- Serverless services like lambda, azure functions, and/or cloud functions.
- Orchestration services like data factory, data pipeline, and/or data flow.
- Data science workbench/managed services like azure machine learning, sagemaker, and/or AI platform.
- Data warehouse services like snowflake, redshift, bigquery, azure sql dw, AWS Redshift.
- Distributed computing services like Pyspark, EMR, Databricks.
- Data storage services like cloud storage, S3, blob, S3 Glacier.
- Data visualization tools like Power BI, Tableau, Quicksight, and/or Qlik.
- Proven experience serving up predictive algorithms and analytics through batch and real-time APIs.
- Solid working experience with software engineers, data scientists, product owners, business analysts, project managers, and business stakeholders to design the holistic solution.
- Strong technical acumen around automated testing.
- Extensive background in statistical analysis and modeling (distributions, hypothesis testing, probability theory, etc.)
- Strong hands-on experience with statistical packages and ML libraries (e.g., Python scikit learn, Spark MLlib, etc.)
- Experience in effective data exploration and visualization (e.g., Excel, Power BI, Tableau, Qlik, etc.)
- Experience in developing and debugging in one or more of the languages Java, Python.
- Ability to work in cross functional teams.
- Apply Machine Learning techniques in production including, but not limited to, neuralnets, regression, decision trees, random forests, ensembles, SVM, Bayesian models, K-Means, etc.

Roles and Responsibilities:

Deploying ML models into production, and scaling them to serve millions of customers.

Technical solutioning skills with deep understanding of technical API integrations, AI / Data Science, BigData and public cloud architectures / deployments in a SaaS environment.

Strong stakeholder relationship management skills - able to influence and manage the expectations of senior executives.
Strong networking skills with the ability to build and maintain strong relationships with both business, operations and technology teams internally and externally.

Provide software design and programming support to projects.

 Qualifications & Experience:

Engineering and post graduate candidates, preferably in Computer Science, from premier institutions with proven work experience as a Machine Learning Architect (Deployments) or a similar role for 5-7 years.